1 Introduction

Wood decomposition is important to redistribute nutrient within ecosystems. But most of our knowledge of this process is based on empirical research on one plant growth form. In tropical and subtropical forests, the contribution of lianas another important plant growth form may also be important to forest carbon and nutrient dynamics. Lianas usually have high stem turnover rates and may produce softer wood with a distinct chemical profile that may decay more rapidly than trees. Although, numerous studies have examined chemical profile of tree and liana leaves, to date, however, the chemical profile and afterlife and decomposition dynamics of lianas remained unknown. In this experiment, we attempt to fill this gap by directly contrasting wood decomposition of 12 liana and 12 tree species in a tropical forest in China. We hypothesized that: 1) liana wood decomposes faster than tree wood; 2) liana wood characteristics differ systematically from tree wood; and 3) microbial decay is higher for liana wood due to its larger vessels and richer nutrients content, and thus have a stronger effect on the wood decay of lianas compared to trees. We used a common garden as depicted in the following figure S2 Figure S2.

Figure S2

1.1 Load needed libraries

library(dplyr)
library(tidyverse)
library(ggplot2)
library(patchwork)
library(lme4)
library(lmerTest)
library(MuMIn)

2 Initial logs trait data before field incubation

Here we load the initial traits (from the file called “Experiment2_data_sheet.csv”) measured for each woody debris (WD) of the 12 species of liana and 12 species of trees. This includes, diameter, bark thickness, initial mass of each WD, the length of each WD used and the unique tag given to each WD before field incubation and the type of litterbag mesh size in which each WD was placed in. Moreover, for each WD, one initial disk was collected, the volumes (dry and wet) of the disk mass of fresh and dry disk (completed at 105 degrees) were measured. We later used this to measure the initial water content and compute the dry mass of each WD prior to incubation.

2.1 Data exploration

Let’s convert some variables to numeric and some to factors.

#convert to numeric

in_dat$diameter_1.size_cm<- as.numeric(in_dat$diameter_1.size_cm)
in_dat$diameter_2.size_cm<- as.numeric(in_dat$diameter_2.size_cm)
in_dat$diameter_3.size_cm<- as.numeric(in_dat$diameter_3.size_cm)
in_dat$Bark.thickness_1._mm<- as.numeric(in_dat$Bark.thickness_1._mm)
in_dat$Bark.thickness_2._mm<- as.numeric(in_dat$Bark.thickness_2._mm)
in_dat$Bark.thickness_3._mm<- as.numeric(in_dat$Bark.thickness_3._mm)
in_dat$Bark.thickness_4_mm<- as.numeric(in_dat$Bark.thickness_4_mm)

in_dat$Wet_weight_of_wood_block_g <- as.numeric(in_dat$Wet_weight_of_wood_block_g)
in_dat$Wet_weight_of_small_pieces_of_wood_g<- as.numeric(in_dat$Wet_weight_of_small_pieces_of_wood_g)
in_dat$Dry_weight_of_small_wood_blocks_g <-as.numeric(in_dat$Dry_weight_of_small_wood_blocks_g)

#convert to factors
in_dat $species<-as.factor(in_dat$species)
in_dat $growth_form<-as.factor(in_dat$growth_form)
in_dat$Tag<-as.factor(in_dat$Tag)
in_dat$species_label<-as.factor(in_dat$species_label)
in_dat$mesh_size<-as.factor(in_dat$mesh_size)
in_dat$family<-as.factor(in_dat$family)
in_dat$diameter_class<- as.factor(in_dat$diameter_class)
summary(in_dat)
dim(in_dat)

2.1.1 Calculate average bark thickness

At the set up, we measured bark thickness at both ends of each log (two readings per end). Here we are going to calculate the average of bark thickness per log.

in_dat$av_bark_thickness<- (in_dat$Bark.thickness_1._mm+in_dat$Bark.thickness_2._mm+in_dat$Bark.thickness_3._mm+in_dat$Bark.thickness_4_mm)/4

2.1.2 Does the average WD density vary with the growth forms?

One argument that might support to why lianas are likely to decompose at a faster rate than trees is the difference in wood densities. From literature, we see that trees have denser woods while lianas woody debris (WD) have wider vessels hence lower density. Here, we test whether this assumption of trees having higher density holds for our wood samples by calculating their wood density and ask: does the average WD wood density vary with the growth forms? We calculate the WD wood density as (mass/volume) for the dry WD subsections.

in_dat$density<- in_dat$Dry_weight_of_small_wood_blocks_g/in_dat$Dry_volume_of_small_wood_blocks.g
summary(in_dat)
in_dat$density<-as.numeric(in_dat$density)
#plot(density~ growth_form, data=in_dat)
anvb<- aov(density~growth_form*diameter_class,data=in_dat)
anova(anvb)
## Analysis of Variance Table
## 
## Response: density
##                              Df  Sum Sq Mean Sq  F value    Pr(>F)    
## growth_form                   1  4.8452  4.8452 430.7779 < 2.2e-16 ***
## diameter_class                1  0.1799  0.1799  15.9938  6.66e-05 ***
## growth_form:diameter_class    1  0.0071  0.0071   0.6327    0.4265    
## Residuals                  1520 17.0961  0.0112                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(anvb)
##                              Df Sum Sq Mean Sq F value   Pr(>F)    
## growth_form                   1  4.845   4.845 430.778  < 2e-16 ***
## diameter_class                1  0.180   0.180  15.994 6.66e-05 ***
## growth_form:diameter_class    1  0.007   0.007   0.633    0.426    
## Residuals                  1520 17.096   0.011                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 12 observations deleted due to missingness

Here we plot this plot using ggplot for better visualization

sdiam<- in_dat[in_dat$diameter_class %in% c("2.5 cm"), ]
anvsm<- aov(density~growth_form,data=sdiam)
anova(anvsm)
## Analysis of Variance Table
## 
## Response: density
##              Df Sum Sq Mean Sq F value    Pr(>F)    
## growth_form   1 2.2858 2.28584  210.59 < 2.2e-16 ***
## Residuals   780 8.4666 0.01085                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(anvsm)
##              Df Sum Sq Mean Sq F value Pr(>F)    
## growth_form   1  2.286  2.2858   210.6 <2e-16 ***
## Residuals   780  8.467  0.0109                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 4 observations deleted due to missingness
lblsdiam <- expression(paste( F["(1, 780)"],"   =   210.6"))
dens_plotsmall<- ggplot(sdiam, aes(x = growth_form, y = density)) +
  geom_jitter(aes(color = growth_form,shape=growth_form), size = 3, alpha = 0.8, width = 0.1)+ scale_y_continuous(limits = c(0,1))+
  scale_shape_manual(values=c(15,17))+
  geom_boxplot(outlier.shape = NA, alpha = 0) +
  labs(y = bquote(bold("Woody debris density " (g/cm^3))), x = "Growth form",colour= "Growth form",title = "2.5 cm diameter WD") +
  theme_classic() +
  annotate("text", x = 1.5, y = 1, size=3, label = as.character(lblsdiam),parse=TRUE) +
  annotate("text", x = 1.5, y = 0.95, size=3, label = "P < 0.001") +
  theme(legend.position = "none" )+
  theme(plot.title=element_text(
    size=12, color="black", vjust = 1, hjust = 0.5),
    axis.text = element_text(size=11, color="black", face = "bold"),
    axis.title = element_text(size=12, color = "black",face = "bold"))+
  scale_color_brewer(palette = "Set1")+
  theme(text = element_text(size = 11,face="bold"), 
        panel.background = element_blank(),
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5))+
  theme(strip.text.x = element_text(size = 10.5))
dens_plotsmall

bdiam<- in_dat[in_dat$diameter_class %in% c("5.0 cm"), ]
anvb<- aov(density~growth_form,data=bdiam)
anova(anvb)
## Analysis of Variance Table
## 
## Response: density
##              Df Sum Sq Mean Sq F value    Pr(>F)    
## growth_form   1 2.5310 2.53097  217.03 < 2.2e-16 ***
## Residuals   740 8.6296 0.01166                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(anvb)
##              Df Sum Sq Mean Sq F value Pr(>F)    
## growth_form   1  2.531  2.5310     217 <2e-16 ***
## Residuals   740  8.630  0.0117                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 8 observations deleted due to missingness
lblbdiam<-expression(paste( F["(1, 740)"],"   =   209.9"))
dens_plotbig<- ggplot(bdiam, aes(x = growth_form, y = density)) +
  geom_jitter(aes(color = growth_form,shape=growth_form), size = 3, alpha = 0.8, width = 0.1)+ scale_y_continuous(limits = c(0,1))+
  scale_shape_manual(values=c(15,17))+
  geom_boxplot(outlier.shape = NA, alpha = 0) +
  labs(y = bquote(bold("Woody debris density " (g/cm^3))), x = "Growth form",colour= "Growth form",title = "4.0 cm diameter WD") +
  theme_classic() +
  annotate("text", x = 1.5, y = 1, size=3, hjust=0.5,label = as.character(lblbdiam),parse=TRUE)+
  annotate("text", x = 1.5, y = 0.95, size=3, label = "P < 0.001") +
  theme(legend.position = "none" )+
  theme(plot.title=element_text(
    size=12, color="black", vjust = 1, hjust = 0.5),
    axis.text = element_text(size=11, color="black"),
    axis.title = element_text(size=12, color = "black"))+
  scale_color_brewer(palette = "Set1")+
  theme(text = element_text(size = 10,face="bold"), 
        panel.background = element_blank(),
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5))+
  theme(strip.text.x = element_text(size = 10.5))
dens_plotbig

dens_plot<- dens_plotsmall+dens_plotbig
path<- getwd()
#ggsave(filename="dens_plot.png", plot=dens_plot, device="png",
#       path=path, height=4, width=8, units="in", dpi=500)
density_mean<- in_dat%>% group_by( growth_form ,species)%>% summarise(mean_density= mean(density, na.rm=TRUE))
density_mean
## # A tibble: 24 × 3
## # Groups:   growth_form [2]
##    growth_form species               mean_density
##    <fct>       <fct>                        <dbl>
##  1 Lianas      Ayenia grandifolia           0.434
##  2 Lianas      Bridelia stipularis          0.481
##  3 Lianas      Calamus henryanus            0.408
##  4 Lianas      Cayratia trifolia            0.483
##  5 Lianas      Celastrus sp1                0.448
##  6 Lianas      Celastrus sp2                0.461
##  7 Lianas      Cheniella touranensis        0.470
##  8 Lianas      Iodes vitiginea              0.448
##  9 Lianas      Piper flaviflorum            0.477
## 10 Lianas      Senegalia pruinescens        0.519
## # ℹ 14 more rows
# Function to classify density within each growth form to low, medium or high
classify_density_by_A <- function(df) {
  low_threshold <- quantile(df$mean_density, 0.33,na.rm=TRUE)
  high_threshold <- quantile(df$mean_density, 0.66,na.rm=TRUE)
  
  df$density_class <- ifelse(df$mean_density <= low_threshold, paste(unique(df$growth_form), "low"),
                             ifelse(df$mean_density <= high_threshold, paste(unique(df$growth_form), "medium"),
                                    paste(unique(df$growth_form), "high")))
  return(df)
}
# Apply the function to each group of A
library(dplyr)
data_density <- density_mean %>%
  group_by(growth_form) %>%
  group_modify(~ classify_density_by_A(.x)) %>%
  ungroup() #important to ungroup after group_modify
data_density 
## # A tibble: 24 × 4
##    growth_form species               mean_density density_class
##    <fct>       <fct>                        <dbl> <chr>        
##  1 Lianas      Ayenia grandifolia           0.434 " low"       
##  2 Lianas      Bridelia stipularis          0.481 " medium"    
##  3 Lianas      Calamus henryanus            0.408 " low"       
##  4 Lianas      Cayratia trifolia            0.483 " high"      
##  5 Lianas      Celastrus sp1                0.448 " low"       
##  6 Lianas      Celastrus sp2                0.461 " medium"    
##  7 Lianas      Cheniella touranensis        0.470 " medium"    
##  8 Lianas      Iodes vitiginea              0.448 " low"       
##  9 Lianas      Piper flaviflorum            0.477 " medium"    
## 10 Lianas      Senegalia pruinescens        0.519 " high"      
## # ℹ 14 more rows

2.1.3 Does the bark thickness vary with growth forms across the different wood diameter?

The other argument we gave as to why Lianas are likely to decompose at a faster rate than trees is the differences in bark proportions, from literature, we see that trees WD bark proportion is lower while lianas WD have higher bark proportion here we test whether this assumption holds for our wood samples by calculating their density does the average WD bark proportion vary with the growth forms does the bark thickness vary with growth forms across the different wood diameter

#calculate average diameter

in_dat$av_wd_diameter<- (in_dat$diameter_2.size_cm+in_dat$diameter_2.size_cm+in_dat$diameter_3.size_cm)/3
in_dat$wd_under_bark<- in_dat$av_wd_diameter-(in_dat$av_bark_thickness/10)
in_dat%>% group_by(diameter_class)%>% summarise(mean=mean(av_wd_diameter, na.rm = TRUE),
                                            sd= sd(av_wd_diameter, na.rm=TRUE),
                                      se=sd(av_wd_diameter,na.rm = TRUE)/sqrt(n())    
                                    )
## # A tibble: 2 × 4
##   diameter_class  mean    sd     se
##   <fct>          <dbl> <dbl>  <dbl>
## 1 2.5 cm          2.59 0.538 0.0192
## 2 5.0 cm          3.92 0.965 0.0352
in_dat%>% group_by(diameter_class)%>% summarise(median=median(av_wd_diameter, na.rm = TRUE) ) 
## # A tibble: 2 × 2
##   diameter_class median
##   <fct>           <dbl>
## 1 2.5 cm           2.53
## 2 5.0 cm           3.87

Measuring bark percentage (check the link below) also see Berendt et al 2021 European Journal of Wood and Wood Products https://fennerschool-associated.anu.edu.au/mensuration/BrackandWood1998/BARK.HTM#. The proportion of fresh bark volume (Vbark) was calculated as the difference between the disc’s fresh volume over bark (Vo.b.) and under bark (Vu.b.) divided by Vo.b.:

The bark to inner wood diameter ratio (bark:wood ratio) was calculated by taking 2x the average bark thickness divided by the diameter of the inner wood.

in_dat$bark_wd_ratio<- 2*in_dat$av_bark_thickness/in_dat$wd_under_bark

#plot(bark_wd_ratio~ growth_form, data=in_dat)
av1<- aov(bark_wd_ratio~growth_form*diameter_class,data=in_dat)
anova(av1)
## Analysis of Variance Table
## 
## Response: bark_wd_ratio
##                              Df Sum Sq Mean Sq  F value Pr(>F)    
## growth_form                   1  99.14  99.143 302.2137 <2e-16 ***
## diameter_class                1   2.15   2.154   6.5649 0.0105 *  
## growth_form:diameter_class    1   0.02   0.019   0.0588 0.8085    
## Residuals                  1467 481.26   0.328                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(av1)
##                              Df Sum Sq Mean Sq F value Pr(>F)    
## growth_form                   1   99.1   99.14 302.214 <2e-16 ***
## diameter_class                1    2.2    2.15   6.565 0.0105 *  
## growth_form:diameter_class    1    0.0    0.02   0.059 0.8085    
## Residuals                  1467  481.3    0.33                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 65 observations deleted due to missingness
av2<- aov(bark_wd_ratio~growth_form+diameter_class,data=in_dat)
anova(av2)
## Analysis of Variance Table
## 
## Response: bark_wd_ratio
##                  Df Sum Sq Mean Sq  F value  Pr(>F)    
## growth_form       1  99.14  99.143 302.4076 < 2e-16 ***
## diameter_class    1   2.15   2.154   6.5691 0.01048 *  
## Residuals      1468 481.28   0.328                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(av2)
##                  Df Sum Sq Mean Sq F value Pr(>F)    
## growth_form       1   99.1   99.14 302.408 <2e-16 ***
## diameter_class    1    2.2    2.15   6.569 0.0105 *  
## Residuals      1468  481.3    0.33                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 65 observations deleted due to missingness
av3<- aov(bark_wd_ratio~growth_form,data=in_dat)
anova(av3)
## Analysis of Variance Table
## 
## Response: bark_wd_ratio
##               Df Sum Sq Mean Sq F value    Pr(>F)    
## growth_form    1  99.14  99.143  301.27 < 2.2e-16 ***
## Residuals   1469 483.43   0.329                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(av3)
##               Df Sum Sq Mean Sq F value Pr(>F)    
## growth_form    1   99.1   99.14   301.3 <2e-16 ***
## Residuals   1469  483.4    0.33                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 65 observations deleted due to missingness

Bark to inner wood ratio is higher for liana species than trees WD.

lblbark_wood_ratio<-expression(paste( F["(1, 1469)"],"   =   301.27"))
bark_wd_ratio_plot<- ggplot(in_dat, aes(x = growth_form, y = bark_wd_ratio)) +
  geom_jitter(aes(color = growth_form,shape=growth_form), size = 3, alpha = 0.8, width = 0.1)+
  scale_shape_manual(values=c(15,17))+
  geom_boxplot(outlier.shape = NA, alpha = 0) +
  labs(y = "Bark to inner wood ratio", x = "Growth forms",colour= "Growth forms") +
  annotate("text", x = 1.5, y = 4.2, size=3, hjust=0.5,label = as.character(lblbark_wood_ratio),parse=TRUE)+
  annotate("text", x = 1.5, y = 4, size=3, label = "P < 0.001") +
  theme(legend.position = "none" )+
  theme(plot.title=element_text(
    size=12, color="black", vjust = 1, hjust = 0.5),
    axis.text = element_text(size=11, color="black"),
    axis.title = element_text(size=12, color = "black"))+
  scale_color_brewer(palette = "Set1")+
  theme(text = element_text(size = 10,face="bold"), 
        panel.background = element_blank(),
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5))
#bark_wd_ratio_plot
#ggsave(filename="bark_wd_ratio.png", plot=bark_wd_ratio_plot, device="png",
#      path=path, height=4, width=5, units="in", dpi=500)

2.1.4 Calculate average density and bark wd ratio for each species

After calculating the density, we now left join the two columns to our dataset.

spc_dens_wdbark<- in_dat %>% group_by(species,diameter_class)%>% 
  summarize(avg_density = mean(density,na.rm = TRUE),
            avg_brkwd_ratio = mean (bark_wd_ratio,na.rm = TRUE), )

in_datb<- in_dat%>% left_join(spc_dens_wdbark, by= c("species","diameter_class"))

2.1.5 Initial dry mass, final dry mass and % mass loss calculations

To avoid altering or changing the wood structure and properties before field incubation, we did not oven dry the logs. Instead to get the approximate dry mass prior to incubation for each log, we got 5cm discs from the logs and weighed their fresh weight, then the discs were oven dried to a constant mass and this was used to estimate the dry mass of the entire log (initial dry mass). The initial dry mass of the logs before field incubation were calculated as in (Seibold et al., 2021) using the following equation: Dry mass= [fresh mass](20 cm log)/[fresh mass](5cm disc) x [dry mass]_(5cm disc)

Percentage mass loss was calculated using the following equation:

ML=(M_initial-M_final)/M_initial x 100 Where M_initial represents the initial dry mass of the WD and M_final represents the final dry for the WD at the retrieval time.

in_dat$initial_mass<- (in_dat$Wet_weight_of_wood_block_g /in_dat$Wet_weight_of_small_pieces_of_wood_g)*in_dat$Dry_weight_of_small_wood_blocks_g
summary(in_dat)

3 Import the chemistry data for the logs before the start of the experiment

3.1 Initial chemical traits data

Each chemical trait [carbon (C), nitrogen (N), phosphorus (P), magnesium (Mg), manganese (Mn), calcium (Ca), potassium (K), silicon (Si), acid-detergent fiber (ADF), acid-detergent lignin (ADL), neutral fiber detergent (NDF), total sugars, condensed tannins)] were measured for each species for the two diameters used in this experiment (~2.0 cm and ~ 4.0 cm diameter). Wood and bark samples were grounded separately to measure the chemical traits. For some species there was no bark that could be used to measure the initial traits and are therefore missing. The initial chemistry is recorded in the file named “Exp1_initial_chemistry.csv”. Latter on, we explain how ADL, NDF, and ADF were used to get lignin, cellulose, and hemicellulose.

in_trait<-read.csv("Exp2_initial_Chemistry.csv", header = TRUE, sep = ",", quote = "\"",
                   dec = ".", fill = TRUE, comment.char = "",fileEncoding = 'latin1')
summary(in_trait)
##    species          growth_form        diameter_class      wood_bark        
##  Length:135         Length:135         Length:135         Length:135        
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##     label            lab_unique         new_label              T_C       
##  Length:135         Length:135         Length:135         Min.   :396.0  
##  Class :character   Class :character   Class :character   1st Qu.:455.0  
##  Mode  :character   Mode  :character   Mode  :character   Median :472.0  
##                                                           Mean   :465.2  
##                                                           3rd Qu.:478.0  
##                                                           Max.   :507.0  
##                                                                          
##       T_N            Tannins          NDF_per         ADF_per     
##  Min.   : 2.260   Min.   :0.0100   Min.   :50.00   Min.   :40.25  
##  1st Qu.: 5.510   1st Qu.:0.1200   1st Qu.:80.44   1st Qu.:62.10  
##  Median : 7.070   Median :0.3400   Median :84.32   Median :68.15  
##  Mean   : 7.985   Mean   :0.9055   Mean   :83.14   Mean   :67.38  
##  3rd Qu.: 9.510   3rd Qu.:1.1250   3rd Qu.:89.58   3rd Qu.:73.07  
##  Max.   :23.180   Max.   :6.0000   Max.   :93.34   Max.   :80.34  
##                   NA's   :4                                       
##     ADL_per         Ca_g_kg           K_g_kg          Mg_g_kg     
##  Min.   : 7.21   Min.   : 1.400   Min.   : 1.470   Min.   :0.290  
##  1st Qu.:16.25   1st Qu.: 6.745   1st Qu.: 3.850   1st Qu.:0.695  
##  Median :18.97   Median :15.700   Median : 5.730   Median :1.220  
##  Mean   :20.43   Mean   :20.793   Mean   : 7.325   Mean   :1.377  
##  3rd Qu.:22.96   3rd Qu.:28.560   3rd Qu.: 9.690   3rd Qu.:1.945  
##  Max.   :39.82   Max.   :76.660   Max.   :25.550   Max.   :3.600  
##                                                                   
##     Mn_mg_kg           P_g_kg       T_sugar_per      T_Si_g_kg    
##  Min.   :   5.10   Min.   :0.180   Min.   :0.110   Min.   : 0.07  
##  1st Qu.:  12.65   1st Qu.:0.620   1st Qu.:0.370   1st Qu.: 0.46  
##  Median :  37.20   Median :0.830   Median :0.870   Median : 0.80  
##  Mean   : 121.90   Mean   :1.245   Mean   :1.191   Mean   : 2.36  
##  3rd Qu.: 104.65   3rd Qu.:1.525   3rd Qu.:1.565   3rd Qu.: 1.86  
##  Max.   :1283.80   Max.   :4.300   Max.   :7.220   Max.   :31.58  
## 
in_trait$species<- as.factor(in_trait$species)
#in_trait$ species_name<- as.factor((in_trait$ species_name))
in_trait$diameter_class<- as.factor(in_trait$diameter_class)
in_trait$wood_bark<-as.factor(in_trait$wood_bark)
in_trait$growth_form<-as.factor(in_trait$growth_form)

Here we calculate cellulose and hemicellulose content.To do so, here we use the equations provided by Chen et al., 2012 plos one. Hemicellulose = Neutral-detergent fiber–Acid-detergent fiber; Cellulose = Acid-detergent fiber -Lignin; Total nonstructural carbohydrate = 1002Neutral-detergent fiber-Crude protein-Lipid-Ash.

in_trait$cellulose <- in_trait$ADF_per- in_trait$ADL_per
in_trait$hemicellulose <- in_trait$NDF_per-in_trait$ADF_per

4 Wood traits

We measured wood traits across woody and growth forms.

4.1 Carbon

in_traitw<- in_trait[in_trait$wood_bark %in% c("wood"), ]
hist(in_traitw$T_C)#not bad

av2<- lmer(T_C~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                             Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## diameter_class             240.365 240.365     1 69.093  4.0674 0.04761 *
## growth_form                 79.244  79.244     1 22.045  1.3409 0.25926  
## diameter_class:growth_form 187.347 187.347     1 69.093  3.1702 0.07939 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r.squaredGLMM(av2)
##             R2m       R2c
## [1,] 0.06160753 0.7583601
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T_C ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitw
## 
## REML criterion at convergence: 704.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.1922 -0.2275  0.0348  0.2926  3.8509 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 170.07   13.041  
##  Residual              60.91    7.805  
## Number of obs: 95, groups:  species, 24
## 
## Fixed effects:
##                      Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)           466.079      4.019  24.052 115.961   <2e-16 ***
## diameter_class5.0 cm    3.225      1.604  70.093   2.011   0.0482 *  
## growth_formTree         6.475      5.560  22.045   1.165   0.2567    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.205       
## grwth_frmTr -0.693  0.004
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                 Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## diameter_class 246.251 246.251     1 70.093  4.0425 0.04822 *
## growth_form     82.605  82.605     1 22.045  1.3561 0.25667  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m<- aov(T_C~growth_form,data=in_traitw)
summary(m)
##             Df Sum Sq Mean Sq F value Pr(>F)  
## growth_form  1    966   966.5   4.325 0.0403 *
## Residuals   93  20783   223.5                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lblwC<-expression(paste( F["(1,93)"],"   =   4.3"))
w_carb<- ggplot(data = in_traitw, aes(x =growth_form, y =T_C/10)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Total carbon (%)", fill= "Growth form", title = "a")+
  annotate("text", x = 1.3, y = 50.5, size=5.5, hjust=0.5,label = as.character(lblwC), parse=TRUE) +
  annotate("text", x = 1.3, y = 49.5, size=5.5, label = "p = 0.04") +
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")

w_carb

This shows higher total carbon in tree wood compared to liana wood.

4.2 Nitrogen

hist(in_traitw$T_N)

hist(log(in_traitw$T_N))#not bad

av2<- lmer(log(T_N)~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq  Mean Sq NumDF  DenDF F value  Pr(>F)  
## diameter_class             0.015383 0.015383     1 69.026  1.1324 0.29097  
## growth_form                0.081201 0.081201     1 22.011  5.9778 0.02295 *
## diameter_class:growth_form 0.004991 0.004991     1 69.026  0.3674 0.54639  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r.squaredGLMM(av2)
##            R2m      R2c
## [1,] 0.1902454 0.923689
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(T_N) ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitw
## 
## REML criterion at convergence: -43.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8212 -0.3051  0.0162  0.3125  2.2337 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.13054  0.3613  
##  Residual             0.01346  0.1160  
## Number of obs: 95, groups:  species, 24
## 
## Fixed effects:
##                      Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)           2.06232    0.10638 22.62003  19.386 1.38e-15 ***
## diameter_class5.0 cm -0.02529    0.02385 70.02564  -1.061    0.293    
## growth_formTree      -0.36518    0.14942 22.01061  -2.444    0.023 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.115       
## grwth_frmTr -0.703  0.002
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)  
## diameter_class 0.015143 0.015143     1 70.026  1.1248 0.2925  
## growth_form    0.080414 0.080414     1 22.011  5.9732 0.0230 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m<- aov(T_N~growth_form,data=in_traitw)
summary(m)
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## growth_form  1  148.2  148.25   18.68 3.88e-05 ***
## Residuals   93  738.3    7.94                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lblwN<-expression(paste( F["(1,93)"],"   =   18.68"))
w_nit<- ggplot(
  data = in_traitw, aes(x = growth_form, y =T_N/10)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Total nitrogen (%)", fill= "Growth form", title = "b")+
  annotate("text", x = 1.6, y = 2, size=5.5, hjust=0.5,label = as.character(lblwN), parse=TRUE) +
  annotate("text", x = 1.6, y = 1.85, size=5.5, label = "p <0.001") +
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")

w_nit

Trees wood have less total nitrogen than liana wood. N does not change with diameter class.

4.3 Condensed tannins

hist(in_traitw$Tannins)

hist(log(in_traitw$Tannins))#not bad

av2<- lmer(log(Tannins)~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                             Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## diameter_class             1.34121 1.34121     1 66.949  5.0424 0.02804 *
## growth_form                0.01039 0.01039     1 21.925  0.0391 0.84514  
## diameter_class:growth_form 0.02315 0.02315     1 66.949  0.0870 0.76888  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Tannins) ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitw
## 
## REML criterion at convergence: 218.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2117 -0.3287 -0.0063  0.3416  3.6958 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 1.8233   1.3503  
##  Residual             0.2624   0.5122  
## Number of obs: 93, groups:  species, 24
## 
## Fixed effects:
##                      Estimate Std. Error      df t value Pr(>|t|)   
## (Intercept)           -1.2946     0.4012 22.8441  -3.226  0.00376 **
## diameter_class5.0 cm  -0.2396     0.1064 67.9484  -2.252  0.02759 * 
## growth_formTree        0.1115     0.5616 21.9261   0.199  0.84442   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.136       
## grwth_frmTr -0.702  0.003
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                 Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## diameter_class 1.33009 1.33009     1 67.948  5.0693 0.02759 *
## growth_form    0.01035 0.01035     1 21.926  0.0394 0.84442  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m<- aov(Tannins~growth_form,data=in_traitw)
summary(m)
##             Df Sum Sq Mean Sq F value Pr(>F)
## growth_form  1   1.79   1.785   1.659  0.201
## Residuals   91  97.93   1.076               
## 2 observations deleted due to missingness
w_cd<- ggplot(data = in_traitw, aes(x = growth_form, y =Tannins)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Condensed tannins (%)", fill= "Growth form",title = "c")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")

w_cd

This shows that together is significant difference of condensed tannin between diameters classes. Liana wood showed no difference in condensed tannin concentration compared to tree wood

4.4 Cellulose

hist(in_traitw$cellulose)#not bad

av2<- lmer(cellulose~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                            Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)    
## diameter_class              0.403   0.403     1 69.058  0.1067 0.744952    
## growth_form                56.886  56.886     1 22.029 15.0753 0.000801 ***
## diameter_class:growth_form  6.046   6.046     1 69.058  1.6021 0.209855    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r.squaredGLMM(av2)
##            R2m       R2c
## [1,] 0.3583204 0.8909903
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: cellulose ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitw
## 
## REML criterion at convergence: 461
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.65277 -0.48545  0.06363  0.45109  1.88003 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 18.428   4.293   
##  Residual              3.806   1.951   
## Number of obs: 95, groups:  species, 24
## 
## Fixed effects:
##                      Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)           46.1063     1.2882 23.2235  35.790  < 2e-16 ***
## diameter_class5.0 cm  -0.1234     0.4010 70.0582  -0.308 0.759195    
## growth_formTree        6.9879     1.7978 22.0291   3.887 0.000793 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.160       
## grwth_frmTr -0.699  0.003
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## diameter_class  0.360   0.360     1 70.058  0.0947 0.7591946    
## growth_form    57.503  57.503     1 22.029 15.1080 0.0007928 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m<- aov(cellulose~growth_form,data=in_traitw)
summary(m)
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## growth_form  1   1162  1162.3   54.85 5.79e-11 ***
## Residuals   93   1971    21.2                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lblwCel<-expression(paste( F["(1,93)"],"   =   13.34"))
w_cel<- ggplot(data = in_traitw, aes(x = growth_form, y =cellulose)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Cellulose (%)", fill= "Growth form", title = "d")+
  annotate("text", x = 1.3, y = 60, size=5.5, hjust=0.5,label = as.character(lblwCel), parse=TRUE) +
  annotate("text", x = 1.3, y = 58, size=5.5, label = "p < 0.001")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")

w_cel

Tree wood has higher cellulose content than liana wood.

4.5 Lignin (ADL)

hist(in_traitw$ADL_per)

hist(log(in_traitw$ADL_per))#not bad

av2<- lmer(log(ADL_per)~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                               Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class             0.0000559 0.0000559     1 68.977  0.0094 0.9232
## growth_form                0.0001082 0.0001082     1 21.959  0.0181 0.8942
## diameter_class:growth_form 0.0148391 0.0148391     1 68.977  2.4847 0.1195
r.squaredGLMM(av2)
##            R2m       R2c
## [1,] 0.0036666 0.8895338
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(ADL_per) ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitw
## 
## REML criterion at convergence: -120.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.8683 -0.5969 -0.0503  0.3161  3.2846 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.048006 0.21910 
##  Residual             0.006093 0.07806 
## Number of obs: 95, groups:  species, 24
## 
## Fixed effects:
##                       Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)           2.989376   0.064799 22.702645  46.133   <2e-16 ***
## diameter_class5.0 cm  0.001193   0.016045 69.975967   0.074    0.941    
## growth_formTree      -0.012557   0.090876 21.957619  -0.138    0.891    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.127       
## grwth_frmTr -0.702  0.002
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                    Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class 3.3702e-05 3.3702e-05     1 69.976  0.0055 0.9409
## growth_form    1.1635e-04 1.1635e-04     1 21.958  0.0191 0.8914
m<- aov(ADL_per~growth_form,data=in_traitw)
summary(m)
##             Df Sum Sq Mean Sq F value Pr(>F)
## growth_form  1    0.2   0.223   0.009  0.926
## Residuals   93 2391.1  25.711
w_adl<- ggplot(data = in_traitw, aes(x = growth_form, y =ADL_per)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Lignin (%)", fill= "Growth form", title = "e")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")

w_adl

Lignin does not vary with growth forms and neither with diameter.

4.6 Hemicellulose

hist(in_traitw$hemicellulose)#not bad

av2<- lmer(hemicellulose~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                             Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## diameter_class              3.0410  3.0410     1 69.017  2.4910 0.119073   
## growth_form                11.8132 11.8132     1 21.992  9.6765 0.005097 **
## diameter_class:growth_form  1.0616  1.0616     1 69.017  0.8696 0.354318   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r.squaredGLMM(av2)
##            R2m       R2c
## [1,] 0.2693525 0.8910145
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: hemicellulose ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitw
## 
## REML criterion at convergence: 359.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4575 -0.4088  0.0447  0.3221  3.7470 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 6.955    2.637   
##  Residual             1.219    1.104   
## Number of obs: 95, groups:  species, 24
## 
## Fixed effects:
##                      Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)           18.3496     0.7869 23.0121  23.318  < 2e-16 ***
## diameter_class5.0 cm   0.3555     0.2269 70.0178   1.567  0.12171    
## growth_formTree       -3.4278     1.1003 21.9928  -3.115  0.00504 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.148       
## grwth_frmTr -0.700  0.003
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                 Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## diameter_class  2.9918  2.9918     1 70.018  2.4543 0.121714   
## growth_form    11.8317 11.8317     1 21.993  9.7058 0.005041 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m<- aov(hemicellulose~growth_form,data=in_traitw)
summary(m)
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## growth_form  1  289.3  289.35   37.48 2.18e-08 ***
## Residuals   93  717.9    7.72                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lblwHemi<-expression(paste( F["(1,93)"],"   =   37.48"))
w_hemc_plot<- ggplot(data = in_traitw, aes(x = growth_form, y =hemicellulose)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+ 
  labs(x= "Growth form", y= "Hemicellulose (%)", fill= "Growth form", title = "f")+
  annotate("text", x = 1.6, y = 24, size=5.5, hjust=0.5,label = as.character(lblwHemi), parse=TRUE) +
  annotate("text", x = 1.6, y = 22.5, size=5.5, label = "p < 0.001")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")

w_hemc_plot

Liana wood has higher hemicellulose level than tree wood

4.7 Calcium

hist(in_traitw$Ca_g_kg)

hist(log(in_traitw$Ca_g_kg))

hist((in_traitw$Ca_g_kg^1/3))

av2<- lmer(log(Ca_g_kg)~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                             Sum Sq Mean Sq NumDF DenDF F value   Pr(>F)   
## diameter_class             0.27721 0.27721     1 69.03  4.0085 0.049202 * 
## growth_form                0.88762 0.88762     1 22.01 12.8350 0.001659 **
## diameter_class:growth_form 0.02956 0.02956     1 69.03  0.4275 0.515401   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r.squaredGLMM(av2)
##            R2m       R2c
## [1,] 0.3315704 0.9179877
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Ca_g_kg) ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitw
## 
## REML criterion at convergence: 99.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1433 -0.3880 -0.0283  0.3786  3.1893 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.49490  0.7035  
##  Residual             0.06858  0.2619  
## Number of obs: 95, groups:  species, 24
## 
## Fixed effects:
##                      Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)           2.89444    0.20851 22.82173  13.882  1.3e-12 ***
## diameter_class5.0 cm -0.10873    0.05383 70.02913  -2.020  0.04722 *  
## growth_formTree      -1.04707    0.29220 22.00913  -3.583  0.00166 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.133       
## grwth_frmTr -0.701  0.003
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                 Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## diameter_class 0.27982 0.27982     1 70.029  4.0802 0.047216 * 
## growth_form    0.88061 0.88061     1 22.009 12.8406 0.001656 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m<- aov(Ca_g_kg~growth_form,data=in_traitw)
summary(m)
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## growth_form  1   2246  2245.6   17.19 7.46e-05 ***
## Residuals   93  12149   130.6                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lblwCa<-expression(paste( F["(1,93)"],"   =   17.19"))

w_cal<- ggplot(data = in_traitw, aes(x = growth_form, y =Ca_g_kg)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Calcium g/kg", fill= "Growth form", title = "g")+
  annotate("text", x = 1.3, y = 70, size=5.5, hjust=0.5,label = as.character(lblwCa), parse=TRUE) +
  annotate("text", x = 1.3, y = 64, size=5.5, label = "p <0.001")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+  theme(legend.position = "none")

w_cal

Liana wood has higher calcium content than tree wood

4.8 Potassium

hist(in_traitw$K_g_kg)

hist(log(in_traitw$K_g_kg))

av2<- lmer(log(in_traitw$K_g_kg)~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq  Mean Sq NumDF  DenDF F value  Pr(>F)  
## diameter_class             0.065870 0.065870     1 69.004  4.6475 0.03459 *
## growth_form                0.062609 0.062609     1 21.999  4.4174 0.04725 *
## diameter_class:growth_form 0.056622 0.056622     1 69.004  3.9950 0.04958 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r.squaredGLMM(av2)
##            R2m       R2c
## [1,] 0.1552977 0.9740135
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(in_traitw$K_g_kg) ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitw
## 
## REML criterion at convergence: -10.4
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.85357 -0.49445 -0.02163  0.48426  2.19844 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.44691  0.6685  
##  Residual             0.01477  0.1216  
## Number of obs: 95, groups:  species, 24
## 
## Fixed effects:
##                      Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)           2.04721    0.19423 22.19729  10.540 4.16e-10 ***
## diameter_class5.0 cm -0.05208    0.02499 70.00392  -2.084   0.0408 *  
## growth_formTree      -0.57499    0.27406 21.99899  -2.098   0.0476 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.066       
## grwth_frmTr -0.706  0.001
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq  Mean Sq NumDF  DenDF F value  Pr(>F)  
## diameter_class 0.064193 0.064193     1 70.004  4.3449 0.04077 *
## growth_form    0.065035 0.065035     1 21.999  4.4018 0.04761 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m<- aov(K_g_kg~growth_form,data=in_traitw)
summary(m)
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## growth_form  1  329.3   329.3   13.02 0.000499 ***
## Residuals   93 2352.7    25.3                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lblwK<-expression(paste( F["(1,93)"],"   =   13.02"))
w_pot<- ggplot(data = in_traitw, aes(x = growth_form, y =K_g_kg)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Potassium g/kg", fill= "Growth form", title = "h")+
  annotate("text", x = 1.6, y = 24, size=5.5, hjust=0.5,label = as.character(lblwK), parse=TRUE) +
  annotate("text", x = 1.6, y = 22, size=5.5, label = "p < 0.001")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+  theme(legend.position = "none")

w_pot

Liana wood has higher potassium content than tree wood

4.9 Magnesium

hist(in_traitw$Mg_g_kg)

hist(log(in_traitw$Mg_g_kg))

av2<- lmer(log(in_traitw$Mg_g_kg)~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                               Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class             0.0074797 0.0074797     1 69.024  0.2489 0.6194
## growth_form                0.0138037 0.0138037     1 22.012  0.4594 0.5050
## diameter_class:growth_form 0.0218273 0.0218273     1 69.024  0.7264 0.3970
r.squaredGLMM(av2)
##             R2m       R2c
## [1,] 0.01866573 0.9271422
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(in_traitw$Mg_g_kg) ~ diameter_class + growth_form + (1 |  
##     species)
##    Data: in_traitw
## 
## REML criterion at convergence: 35.4
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -3.09992 -0.42232  0.04957  0.39121  2.49347 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.37468  0.6121  
##  Residual             0.02993  0.1730  
## Number of obs: 95, groups:  species, 24
## 
## Fixed effects:
##                      Estimate Std. Error       df t value Pr(>|t|)
## (Intercept)           0.02045    0.17944 22.48609   0.114    0.910
## diameter_class5.0 cm  0.01820    0.03556 70.02329   0.512    0.610
## growth_formTree      -0.17066    0.25241 22.01156  -0.676    0.506
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.102       
## grwth_frmTr -0.704  0.002
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class 0.007843 0.007843     1 70.023  0.2620 0.6104
## growth_form    0.013684 0.013684     1 22.012  0.4571 0.5060
m<- aov(Mg_g_kg~growth_form,data=in_traitw)
summary(m)
##             Df Sum Sq Mean Sq F value Pr(>F)
## growth_form  1   0.77  0.7706    1.96  0.165
## Residuals   93  36.56  0.3931
w_mag<- ggplot(data = in_traitw, aes(x = growth_form, y =Mg_g_kg)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Magnesium g/kg", fill= "Growth form", title = "i")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+  theme(legend.position = "none")
w_mag

There is no significance difference in liana and tree wood magnesium content

4.10 Managanese

hist(in_traitw$Mn_mg_kg)

hist(log(in_traitw$Mn_mg_kg))

av2<- lmer(log(log(in_traitw$Mn_mg_kg))~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                               Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class             0.0003616 0.0003616     1 69.024  0.0246 0.8759
## growth_form                0.0055796 0.0055796     1 22.006  0.3791 0.5444
## diameter_class:growth_form 0.0024372 0.0024372     1 69.024  0.1656 0.6853
r.squaredGLMM(av2)
##             R2m       R2c
## [1,] 0.01475177 0.8967064
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(log(in_traitw$Mn_mg_kg)) ~ diameter_class + growth_form +  
##     (1 | species)
##    Data: in_traitw
## 
## REML criterion at convergence: -38.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.35441 -0.48013  0.04549  0.53244  2.26634 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.12567  0.3545  
##  Residual             0.01454  0.1206  
## Number of obs: 95, groups:  species, 24
## 
## Fixed effects:
##                      Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)           1.13355    0.10462 22.68835  10.834 1.93e-10 ***
## diameter_class5.0 cm -0.00377    0.02479 70.02319  -0.152    0.880    
## growth_formTree       0.09056    0.14683 22.00638   0.617    0.544    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.122       
## grwth_frmTr -0.702  0.002
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                   Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class 0.0003364 0.0003364     1 70.023  0.0231 0.8796
## growth_form    0.0055326 0.0055326     1 22.006  0.3804 0.5437
m<- aov(Mn_mg_kg~growth_form,data=in_traitw)
summary(m)
##             Df  Sum Sq Mean Sq F value Pr(>F)
## growth_form  1   10133   10133   0.479  0.491
## Residuals   93 1967098   21152
w_mang<- ggplot(data = in_traitw, aes(x = growth_form, y =Mn_mg_kg/1000)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Manganese g/kg", fill= "Growth form", title = "j")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+  theme(legend.position = "none")

w_mang

Manganese content does not differ between liana and tree wood.

4.11 Phosphorous

hist(in_traitw$P_g_kg)

hist(log(in_traitw$P_g_kg+2))

av2<- lmer(log(in_traitw$P_g_kg+2)~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                                Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class             0.00084713 0.00084713     1 69.010  0.3530 0.5544
## growth_form                0.00000178 0.00000178     1 22.005  0.0007 0.9785
## diameter_class:growth_form 0.00081716 0.00081716     1 69.010  0.3405 0.5614
r.squaredGLMM(av2)
##               R2m       R2c
## [1,] 0.0002740362 0.9674408
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(in_traitw$P_g_kg + 2) ~ diameter_class + growth_form + (1 |  
##     species)
##    Data: in_traitw
## 
## REML criterion at convergence: -178.6
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.60584 -0.36674 -0.02784  0.32741  3.14176 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.071289 0.26700 
##  Residual             0.002377 0.04876 
## Number of obs: 95, groups:  species, 24
## 
## Fixed effects:
##                       Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)           1.130720   0.077577 22.204738  14.575  7.5e-13 ***
## diameter_class5.0 cm  0.006066   0.010022 70.009647   0.605    0.547    
## growth_formTree      -0.002895   0.109462 22.004678  -0.026    0.979    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.066       
## grwth_frmTr -0.706  0.001
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                   Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class 8.708e-04 8.708e-04     1 70.010  0.3663 0.5470
## growth_form    1.660e-06 1.660e-06     1 22.005  0.0007 0.9791
m<- aov(P_g_kg~growth_form,data=in_traitw)
summary(m)
##             Df Sum Sq Mean Sq F value Pr(>F)
## growth_form  1   0.15  0.1505   0.156  0.694
## Residuals   93  89.78  0.9654
w_ph<- ggplot(data = in_traitw, aes(x = growth_form, y =P_g_kg)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Phosphorous g/kg", fill= "Growth form", title = "k")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+  theme(legend.position = "none")
w_ph

Phosphorous content does not significantly differ between liana and tree wood.

4.12 Total sugars

hist(in_traitw$T_sugar_per)

hist(log(in_traitw$T_sugar_per+2))

av2<- lmer(log(in_traitw$T_sugar_per+2)~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq  Mean Sq NumDF  DenDF F value   Pr(>F)   
## diameter_class             0.000566 0.000566     1 69.017  0.1310 0.718513   
## growth_form                0.035532 0.035532     1 22.005  8.2278 0.008928 **
## diameter_class:growth_form 0.000009 0.000009     1 69.017  0.0021 0.963728   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r.squaredGLMM(av2)
##            R2m       R2c
## [1,] 0.2463279 0.9422445
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(in_traitw$T_sugar_per + 2) ~ diameter_class + growth_form +  
##     (1 | species)
##    Data: in_traitw
## 
## REML criterion at convergence: -144.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8121 -0.2318  0.0231  0.3646  4.5056 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.052049 0.22814 
##  Residual             0.004257 0.06525 
## Number of obs: 95, groups:  species, 24
## 
## Fixed effects:
##                       Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)           0.948391   0.066903 22.490040  14.176 1.07e-12 ***
## diameter_class5.0 cm -0.004896   0.013411 70.016567  -0.365  0.71613    
## growth_formTree       0.269915   0.094099 22.004557   2.868  0.00893 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.103       
## grwth_frmTr -0.704  0.002
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq  Mean Sq NumDF  DenDF F value   Pr(>F)   
## diameter_class 0.000567 0.000567     1 70.017  0.1333 0.716133   
## growth_form    0.035026 0.035026     1 22.005  8.2277 0.008928 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m<- aov(T_sugar_per~growth_form,data=in_traitw)
summary(m)
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## growth_form  1  19.85   19.85   27.19 1.11e-06 ***
## Residuals   93  67.90    0.73                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lblwTsug<-expression(paste( F["(1,93)"],"   =   27.19"))
w_sugar<- ggplot(data = in_traitw, aes(x = growth_form, y =T_sugar_per)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Total sugars (%)", fill= "Growth form", title = "l")+
  annotate("text", x = 1.3, y = 4.2, size=5.5, hjust=0.5,label = as.character(lblwTsug), parse=TRUE) +
  annotate("text", x = 1.3, y = 3.8, size=5.5, label = "p < 0.001")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+  theme(legend.position = "none")
w_sugar

Tree wood has more total sugars than liana wood.

4.13 Silicon

hist(in_traitw$T_Si_g_kg)

hist(log(in_traitw$T_Si_g_kg+2))

av2<- lmer(log(in_traitw$T_Si_g_kg+2)~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                               Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class             0.0149540 0.0149540     1 69.019  1.0252 0.3148
## growth_form                0.0002134 0.0002134     1 22.010  0.0146 0.9048
## diameter_class:growth_form 0.0006448 0.0006448     1 69.019  0.0442 0.8341
r.squaredGLMM(av2)
##              R2m       R2c
## [1,] 0.001258852 0.9395454
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(in_traitw$T_Si_g_kg + 2) ~ diameter_class + growth_form +  
##     (1 | species)
##    Data: in_traitw
## 
## REML criterion at convergence: -27
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2038 -0.5694 -0.0084  0.5695  2.2151 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.22643  0.4758  
##  Residual             0.01439  0.1199  
## Number of obs: 95, groups:  species, 24
## 
## Fixed effects:
##                      Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)           1.12284    0.13906 22.38821   8.075 4.45e-08 ***
## diameter_class5.0 cm  0.02507    0.02465 70.01886   1.017    0.313    
## growth_formTree       0.02362    0.19582 22.00949   0.121    0.905    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.091       
## grwth_frmTr -0.704  0.002
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                   Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class 0.0148703 0.0148703     1 70.019  1.0336 0.3128
## growth_form    0.0002092 0.0002092     1 22.009  0.0145 0.9051
m<- aov(T_Si_g_kg~growth_form,data=in_traitw)
summary(m)
##             Df Sum Sq Mean Sq F value Pr(>F)
## growth_form  1    5.7   5.737   0.694  0.407
## Residuals   93  768.4   8.263
w_silicon<- ggplot(data = in_traitw, aes(x = growth_form, y =T_sugar_per)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Silicon (g/kg)", fill= "Growth form", title = "m")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+  theme(legend.position = "none")
w_silicon

No difference across diameter nor across growth forms.

library(patchwork)
w_intrait_plot<- w_carb+w_nit+w_cd+w_cel+w_adl+w_hemc_plot+w_cal+w_pot+w_mag+w_mang+w_ph+w_sugar+w_silicon


##   Let plot initial wood traits for lianas and trees. (a) Carbon, (b) Nitrogen, (c) condensed tannins, (d) cellulose, (e) lignin, (f) Hemicellulose, (g) Calcium, (h) Potassium (i) Magnesium, (j)manganese (k) Phosphorus, (l) Total sugars, (m) Silicon.

ggsave(filename="Figure S3.png", plot=w_intrait_plot, device="png",
       path=path, height=12, width=15, units="in", dpi=500)

5 Bark trait

We measured bark traits across woody and growth forms for species that have bark.

5.1 Carbon

in_traitb<- in_trait[in_trait$wood_bark %in% c("bark"), ]
hist(in_traitb$T_C)#not bad

av2<- lmer(T_C~ diameter_class*growth_form +(1|species),data = in_traitb)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                            Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class             62.376  62.376     1 17.107  1.8944 0.1865
## growth_form                25.797  25.797     1 20.092  0.7835 0.3866
## diameter_class:growth_form  1.164   1.164     1 17.107  0.0353 0.8531
r.squaredGLMM(av2)
##             R2m       R2c
## [1,] 0.03521574 0.9646415
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T_C ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitb
## 
## REML criterion at convergence: 316.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.30733 -0.17891  0.00287  0.27712  1.45225 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 863.4    29.384  
##  Residual              31.2     5.586  
## Number of obs: 40, groups:  species, 22
## 
## Fixed effects:
##                      Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)           444.628      9.168  21.737  48.500   <2e-16 ***
## diameter_class5.0 cm    4.088      2.810  18.053   1.455    0.163    
## growth_formTree        11.396     12.681  19.992   0.899    0.380    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.214       
## grwth_frmTr -0.698  0.037
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class 66.030  66.030     1 18.053  2.1163 0.1629
## growth_form    25.199  25.199     1 19.992  0.8076 0.3795
m<- aov(T_C~growth_form,data=in_traitb)
summary(m)
##             Df Sum Sq Mean Sq F value Pr(>F)  
## growth_form  1   2279  2279.0   2.858 0.0991 .
## Residuals   38  30301   797.4                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
b_carb<- ggplot(data = in_traitb, aes(x =growth_form, y =T_C/10)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Total carbon (%)", fill= "Growth form", title = "a")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")

b_carb

There is no significant differences of total carbon between liana and tree bark.

5.2 Nitrogen

hist(in_traitb$T_N)

hist(log(in_traitb$T_N))#not bad

av2<- lmer(log(T_N)~ diameter_class*growth_form +(1|species),data = in_traitb)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                               Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class             0.0078425 0.0078425     1 17.839  0.5627 0.4630
## growth_form                0.0174376 0.0174376     1 19.804  1.2511 0.2767
## diameter_class:growth_form 0.0000501 0.0000501     1 17.839  0.0036 0.9528
r.squaredGLMM(av2)
##             R2m       R2c
## [1,] 0.05257589 0.9315963
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(T_N) ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitb
## 
## REML criterion at convergence: 15.7
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.55550 -0.26883 -0.02211  0.23161  2.77756 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.17852  0.4225  
##  Residual             0.01321  0.1150  
## Number of obs: 40, groups:  species, 22
## 
## Fixed effects:
##                      Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)           2.38741    0.13613 23.21718  17.538 6.85e-15 ***
## diameter_class5.0 cm -0.04516    0.05698 19.08750  -0.793    0.438    
## growth_formTree      -0.20852    0.18457 19.96852  -1.130    0.272    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.292       
## grwth_frmTr -0.690  0.052
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                   Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class 0.0083006 0.0083006     1 19.087  0.6282 0.4378
## growth_form    0.0168656 0.0168656     1 19.968  1.2763 0.2720
m<- aov(T_N~growth_form,data=in_traitb)
summary(m)
##             Df Sum Sq Mean Sq F value Pr(>F)
## growth_form  1   54.0   54.03   2.434  0.127
## Residuals   38  843.7   22.20
b_nit<- ggplot(
  data = in_traitb, aes(x = growth_form, y =T_N/10)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Total nitrogen (%)", fill= "Growth form", title = "b")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")

b_nit

Bark N does not change with diameter classes. Tree bark N not different from liana bark N.

5.3 Condensed tannins

hist(in_traitb$Tannins)

hist(log(in_traitb$Tannins))#not bad

av2<- lmer(log(Tannins)~ diameter_class*growth_form +(1|species),data = in_traitb)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                             Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## diameter_class             0.11843 0.11843     1 18.814  0.6376 0.43456  
## growth_form                0.64084 0.64084     1 19.665  3.4500 0.07829 .
## diameter_class:growth_form 0.00012 0.00012     1 18.814  0.0006 0.98032  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Tannins) ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitb
## 
## REML criterion at convergence: 99.1
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.84535 -0.33664  0.01907  0.23322  1.62178 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 1.6416   1.2813  
##  Residual             0.1754   0.4189  
## Number of obs: 38, groups:  species, 21
## 
## Fixed effects:
##                      Estimate Std. Error      df t value Pr(>|t|)  
## (Intercept)           -0.9480     0.4267 24.0028  -2.221   0.0360 *
## diameter_class5.0 cm   0.1808     0.2187 19.7520   0.827   0.4184  
## growth_formTree        1.0830     0.5798 19.5087   1.868   0.0769 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.358       
## grwth_frmTr -0.664  0.062
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                 Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## diameter_class 0.11986 0.11986     1 19.752  0.6832 0.41837  
## growth_form    0.61223 0.61223     1 19.509  3.4895 0.07685 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m<- aov(Tannins~growth_form,data=in_traitb)
summary(m)
##             Df Sum Sq Mean Sq F value  Pr(>F)   
## growth_form  1  21.39  21.386   10.72 0.00234 **
## Residuals   36  71.81   1.995                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingness
anova(m)
## Analysis of Variance Table
## 
## Response: Tannins
##             Df Sum Sq Mean Sq F value   Pr(>F)   
## growth_form  1 21.386 21.3860  10.721 0.002345 **
## Residuals   36 71.810  1.9947                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lblbTan<-expression(paste( F["(1,36)"],"   =   10.72"))
b_cd<- ggplot(data = in_traitb, aes(x = growth_form, y =Tannins)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Condensed tannins (%)", fill= "Growth form",title = "c")+
  annotate("text", x = 1.3, y = 5.5, size=5.5, hjust=0.5,label = as.character(lblbTan), parse=TRUE) +
  annotate("text", x = 1.3, y = 4.9, size=5.5, label = "p = 0.002")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")

b_cd

This shows that there is no significant differences of condensed tannin between diameters classes. Liana bark have lesser condensed tannin concentration than tree bark.

5.4 Cellulose

hist(in_traitb$cellulose)#not bad

av2<- lmer(cellulose~ diameter_class*growth_form +(1|species),data = in_traitb)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                            Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## diameter_class              0.708   0.708     1 17.760  0.1091 0.74499  
## growth_form                49.042  49.042     1 18.724  7.5657 0.01283 *
## diameter_class:growth_form  0.619   0.619     1 17.760  0.0955 0.76089  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r.squaredGLMM(av2)
##            R2m       R2c
## [1,] 0.2564428 0.9211743
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: cellulose ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitb
## 
## REML criterion at convergence: 235.1
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.46359 -0.48893  0.07288  0.42758  1.32005 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 56.022   7.485   
##  Residual              6.004   2.450   
## Number of obs: 40, groups:  species, 22
## 
## Fixed effects:
##                      Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)           35.9307     2.4742 23.6568  14.522  2.8e-13 ***
## diameter_class5.0 cm   0.5095     1.2002 19.2621   0.424    0.676    
## growth_formTree        9.1567     3.3036 19.2630   2.772    0.012 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.338       
## grwth_frmTr -0.684  0.061
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## diameter_class  1.082   1.082     1 19.262  0.1802 0.67591  
## growth_form    46.126  46.126     1 19.263  7.6823 0.01205 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m<- aov(cellulose~growth_form,data=in_traitb)
summary(m)
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## growth_form  1  676.7   676.7   13.34 0.000781 ***
## Residuals   38 1927.7    50.7                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lblbCel<-expression(paste( F["(1,38)"],"   =   13.34"))
b_cel<- ggplot(data = in_traitb, aes(x = growth_form, y =cellulose)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Cellulose (%)", fill= "Growth form", title = "d")+
  annotate("text", x = 1.3, y = 55, size=5.5, hjust=0.5,label = as.character(lblbCel), parse=TRUE) +
  annotate("text", x = 1.3, y = 51, size=5.5, label = "p < 0.001")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")

b_cel

Liana bark has lesser cellulose than tree bark.

5.5 Lignin (ADL)

hist(in_traitb$ADL_per)

hist(log(in_traitb$ADL_per))#not bad

av2<- lmer(log(ADL_per)~ diameter_class*growth_form +(1|species),data = in_traitb)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq  Mean Sq NumDF  DenDF F value   Pr(>F)   
## diameter_class             0.030638 0.030638     1 23.681  1.0385 0.318468   
## growth_form                0.293775 0.293775     1 21.093  9.9578 0.004752 **
## diameter_class:growth_form 0.070988 0.070988     1 23.681  2.4062 0.134115   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r.squaredGLMM(av2)
##            R2m       R2c
## [1,] 0.3346431 0.8521086
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(ADL_per) ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitb
## 
## REML criterion at convergence: 22.3
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.63713 -0.25683  0.01545  0.32323  1.79775 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.10742  0.3277  
##  Residual             0.03056  0.1748  
## Number of obs: 40, groups:  species, 22
## 
## Fixed effects:
##                      Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)           3.25574    0.12098 28.17289  26.912  < 2e-16 ***
## diameter_class5.0 cm -0.09629    0.08126 24.46949  -1.185  0.24745    
## growth_formTree      -0.50630    0.15222 20.51733  -3.326  0.00328 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.470       
## grwth_frmTr -0.662  0.090
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                 Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## diameter_class 0.04290 0.04290     1 24.470  1.4039 0.247447   
## growth_form    0.33807 0.33807     1 20.517 11.0630 0.003283 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m<- aov(ADL_per~growth_form,data=in_traitb)
summary(m)
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## growth_form  1   1016  1016.3    17.7 0.000152 ***
## Residuals   38   2182    57.4                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lblbAdl<-expression(paste( F["(1,38)"],"   =   17.7"))

b_adl<- ggplot(data = in_traitb, aes(x = growth_form, y =ADL_per)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Lignin (%)", fill= "Growth form", title = "e")+
  annotate("text", x = 1.6, y = 38, size=5.5, hjust=0.5,label = as.character(lblbAdl), parse=TRUE) +
  annotate("text", x = 1.6, y = 35, size=5.5, label = "p < 0.001")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")

b_adl

Lignin (ADL) varies with growth forms and is higher in liana bark than trees.

5.6 Hemicellulose

hist(in_traitb$hemicellulose)#not bad

av2<- lmer(hemicellulose~ diameter_class*growth_form +(1|species),data = in_traitb)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                             Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class             0.01239 0.01239     1 17.380  0.0303 0.8638
## growth_form                0.42724 0.42724     1 20.066  1.0452 0.3188
## diameter_class:growth_form 0.66867 0.66867     1 17.380  1.6359 0.2177
r.squaredGLMM(av2)
##             R2m       R2c
## [1,] 0.05771307 0.9555336
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: hemicellulose ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitb
## 
## REML criterion at convergence: 151
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.72789 -0.30004  0.00813  0.35328  1.68709 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 8.4700   2.9103  
##  Residual             0.4117   0.6416  
## Number of obs: 40, groups:  species, 22
## 
## Fixed effects:
##                       Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)          13.709127   0.918006 22.265450  14.934 4.38e-13 ***
## diameter_class5.0 cm  0.003701   0.321164 18.407243   0.012    0.991    
## growth_formTree      -1.378562   1.261065 19.991834  -1.093    0.287    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.244       
## grwth_frmTr -0.695  0.043
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                 Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class 0.00005 0.00005     1 18.407  0.0001 0.9909
## growth_form    0.49199 0.49199     1 19.992  1.1950 0.2873
m<- aov(hemicellulose~growth_form,data=in_traitb)
summary(m)
##             Df Sum Sq Mean Sq F value Pr(>F)
## growth_form  1  12.61  12.611   1.581  0.216
## Residuals   38 303.20   7.979
b_hemc_plot<- ggplot(data = in_traitb, aes(x = growth_form, y =hemicellulose)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+ 
  labs(x= "Growth form", y= "Hemicellulose (%)", fill= "Growth form", title = "f")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")

b_hemc_plot

No differences across diameters nor across growth forms.

5.7 Calcium

hist(in_traitb$Ca_g_kg)

hist(log(in_traitb$Ca_g_kg))

hist((in_traitb$Ca_g_kg^1/3))

av2<- lmer(log(Ca_g_kg)~ diameter_class*growth_form +(1|species),data = in_traitb)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq  Mean Sq NumDF  DenDF F value  Pr(>F)  
## diameter_class             0.007923 0.007923     1 18.115  0.3022 0.58923  
## growth_form                0.100130 0.100130     1 20.066  3.8188 0.06476 .
## diameter_class:growth_form 0.010900 0.010900     1 18.115  0.4157 0.52716  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r.squaredGLMM(av2)
##            R2m       R2c
## [1,] 0.1529171 0.9385233
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Ca_g_kg) ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitb
## 
## REML criterion at convergence: 39.4
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.89996 -0.25797 -0.02341  0.21462  2.67072 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.33273  0.5768  
##  Residual             0.02556  0.1599  
## Number of obs: 40, groups:  species, 22
## 
## Fixed effects:
##                      Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)           3.67943    0.18627 23.18238  19.753 5.29e-16 ***
## diameter_class5.0 cm -0.05086    0.07917 19.01995  -0.642   0.5283    
## growth_formTree      -0.50935    0.25220 19.82558  -2.020   0.0572 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.296       
## grwth_frmTr -0.689  0.052
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq  Mean Sq NumDF  DenDF F value  Pr(>F)  
## diameter_class 0.010549 0.010549     1 19.020  0.4127 0.52828  
## growth_form    0.104259 0.104259     1 19.826  4.0787 0.05715 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m<- aov(Ca_g_kg~growth_form,data=in_traitb)
summary(m)
##             Df Sum Sq Mean Sq F value  Pr(>F)   
## growth_form  1   2093  2093.3   8.356 0.00632 **
## Residuals   38   9520   250.5                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lblbCa<-expression(paste( F["(1,38)"],"   =   8.3"))

b_cal<- ggplot(data = in_traitb, aes(x = growth_form, y =Ca_g_kg)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Calcium g/kg", fill= "Growth form", title = "g")+
  annotate("text", x = 1.5, y = 75, size=5.5, hjust=0.5,label = as.character(lblbCa), parse=TRUE) +
  annotate("text", x = 1.5, y = 68, size=5.5, label = "p = 0.006")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+  theme(legend.position = "none")

b_cal

More calcium in liana bark than tree bark.

5.8 Potassium

hist(in_traitb$K_g_kg)

hist(log(in_traitb$K_g_kg))

av2<- lmer(log(in_traitb$K_g_kg)~ diameter_class*growth_form +(1|species),data = in_traitb)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class             0.001476 0.001476     1 19.992  0.0399 0.8438
## growth_form                0.091864 0.091864     1 20.433  2.4803 0.1306
## diameter_class:growth_form 0.042522 0.042522     1 19.992  1.1481 0.2967
r.squaredGLMM(av2)
##            R2m      R2c
## [1,] 0.1174186 0.891453
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(in_traitb$K_g_kg) ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitb
## 
## REML criterion at convergence: 42
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.39082 -0.38256  0.03438  0.32239  2.46075 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.25622  0.5062  
##  Residual             0.03841  0.1960  
## Number of obs: 40, groups:  species, 22
## 
## Fixed effects:
##                      Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)           2.03337    0.17247 25.69391  11.790 7.27e-12 ***
## diameter_class5.0 cm -0.02905    0.09464 21.32414  -0.307    0.762    
## growth_formTree      -0.38754    0.22632 20.25780  -1.712    0.102    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.383       
## grwth_frmTr -0.677  0.070
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class 0.003618 0.003618     1 21.324  0.0942 0.7619
## growth_form    0.112626 0.112626     1 20.258  2.9322 0.1021
m<- aov(K_g_kg~growth_form,data=in_traitb)
summary(m)
##             Df Sum Sq Mean Sq F value Pr(>F)  
## growth_form  1  128.3   128.3   5.834 0.0206 *
## Residuals   38  836.0    22.0                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lblbK<-expression(paste( F["(1,38)"],"   =   5.83"))
b_pot<- ggplot(data = in_traitb, aes(x = growth_form, y =K_g_kg)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Potassium g/kg", fill= "Growth form", title = "h")+
  annotate("text", x = 1.6, y = 20, size=5.5, hjust=0.5,label = as.character(lblbK), parse=TRUE) +
  annotate("text", x = 1.6, y = 18, size=5.5, label = "p = 0.02")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+  theme(legend.position = "none")

b_pot

More potassium in liana bark than tree bark.

5.9 Magnesium

hist(in_traitb$Mg_g_kg)

hist(log(in_traitb$Mg_g_kg))

av2<- lmer(log(in_traitb$Mg_g_kg)~ diameter_class*growth_form +(1|species),data = in_traitb)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                               Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class             0.0245715 0.0245715     1 17.506  1.5274 0.2328
## growth_form                0.0001577 0.0001577     1 19.909  0.0098 0.9221
## diameter_class:growth_form 0.0066943 0.0066943     1 17.506  0.4161 0.5272
r.squaredGLMM(av2)
##              R2m       R2c
## [1,] 0.006964865 0.9433095
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(in_traitb$Mg_g_kg) ~ diameter_class + growth_form + (1 |  
##     species)
##    Data: in_traitb
## 
## REML criterion at convergence: 26.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.7869 -0.3866 -0.0071  0.3462  1.9990 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.25958  0.5095  
##  Residual             0.01596  0.1263  
## Number of obs: 40, groups:  species, 22
## 
## Fixed effects:
##                      Estimate Std. Error       df t value Pr(>|t|)   
## (Intercept)           0.58787    0.16248 22.72210   3.618  0.00147 **
## diameter_class5.0 cm -0.08388    0.06292 18.71061  -1.333  0.19847   
## growth_formTree      -0.03248    0.22168 19.93586  -0.147  0.88499   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.270       
## grwth_frmTr -0.692  0.047
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                   Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class 0.0283674 0.0283674     1 18.711  1.7774 0.1985
## growth_form    0.0003426 0.0003426     1 19.936  0.0215 0.8850
m<- aov(Mg_g_kg~growth_form,data=in_traitb)
summary(m)
##             Df Sum Sq Mean Sq F value Pr(>F)
## growth_form  1  0.044  0.0438   0.058   0.81
## Residuals   38 28.554  0.7514
b_mag<- ggplot(data = in_traitb, aes(x = growth_form, y =Mg_g_kg)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Magnesium g/kg", fill= "Growth form", title = "i")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+  theme(legend.position = "none")
b_mag

No difference across diameters nor across growth forms.

5.10 Manganese

hist(in_traitb$Mn_mg_kg)

hist(log(in_traitb$Mn_mg_kg))

av2<- lmer(log(log(in_traitb$Mn_mg_kg))~ diameter_class*growth_form +(1|species),data = in_traitb)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class             0.003802 0.003802     1 25.846  0.1323 0.7190
## growth_form                0.085361 0.085361     1 18.961  2.9710 0.1010
## diameter_class:growth_form 0.068354 0.068354     1 25.846  2.3791 0.1351
r.squaredGLMM(av2)
##           R2m       R2c
## [1,] 0.163154 0.7069552
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(log(in_traitb$Mn_mg_kg)) ~ diameter_class + growth_form +  
##     (1 | species)
##    Data: in_traitb
## 
## REML criterion at convergence: 11.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1929 -0.2308  0.0632  0.2838  1.8516 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.06176  0.2485  
##  Residual             0.02748  0.1658  
## Number of obs: 40, groups:  species, 22
## 
## Fixed effects:
##                      Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)           1.29650    0.09893 28.54959  13.105 1.33e-13 ***
## diameter_class5.0 cm  0.04209    0.07437 25.96757   0.566   0.5763    
## growth_formTree       0.22967    0.12026 19.08876   1.910   0.0713 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.526       
## grwth_frmTr -0.650  0.105
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)  
## diameter_class 0.008801 0.008801     1 25.968  0.3203 0.5763  
## growth_form    0.100233 0.100233     1 19.089  3.6474 0.0713 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m<- aov(Mn_mg_kg~growth_form,data=in_traitb)
summary(m)
##             Df  Sum Sq Mean Sq F value  Pr(>F)   
## growth_form  1  758842  758842   9.126 0.00449 **
## Residuals   38 3159750   83151                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lblbMn<-expression(paste( F["(1,38)"],"   =   9.12"))
b_mang<- ggplot(data = in_traitb, aes(x = growth_form, y =Mn_mg_kg/1000)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Manganese g/kg", fill= "Growth form", title = "j")+
  annotate("text", x = 1.3, y = 1.2, size=5.5, hjust=0.5,label = as.character(lblbMn), parse=TRUE) +
  annotate("text", x = 1.3, y = 1.1, size=5.5, label = "p = 0.04")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+  theme(legend.position = "none")

b_mang

More manganese in tree bark than liana bark.

5.11 Phosphorous

hist(in_traitb$P_g_kg)

hist(log(in_traitb$P_g_kg+2))

av2<- lmer(P_g_kg~ diameter_class*growth_form +(1|species),data = in_traitb)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                               Sum Sq   Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class             0.0012395 0.0012395     1 17.215  0.0462 0.8324
## growth_form                0.0000001 0.0000001     1 20.290  0.0000 0.9989
## diameter_class:growth_form 0.0073247 0.0073247     1 17.215  0.2729 0.6080
r.squaredGLMM(av2)
##               R2m       R2c
## [1,] 0.0006769956 0.9666669
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: P_g_kg ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitb
## 
## REML criterion at convergence: 55.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.94394 -0.14782 -0.08468  0.14070  2.15817 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.77348  0.8795  
##  Residual             0.02589  0.1609  
## Number of obs: 40, groups:  species, 22
## 
## Fixed effects:
##                      Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)           1.19423    0.27373 21.81149   4.363 0.000253 ***
## diameter_class5.0 cm -0.01273    0.08102 18.17461  -0.157 0.876879    
## growth_formTree       0.01135    0.37921 20.18898   0.030 0.976409    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.206       
## grwth_frmTr -0.698  0.036
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                    Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class 0.00063909 0.00063909     1 18.175  0.0247 0.8769
## growth_form    0.00002320 0.00002320     1 20.189  0.0009 0.9764
m<- aov(P_g_kg~growth_form,data=in_traitb)
summary(m)
##             Df Sum Sq Mean Sq F value Pr(>F)
## growth_form  1   0.24  0.2388    0.25   0.62
## Residuals   38  36.32  0.9558
b_ph<- ggplot(data = in_traitb, aes(x = growth_form, y =P_g_kg)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Phosphorous g/kg", fill= "Growth form", title = "k")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+  theme(legend.position = "none")
b_ph

No differences across diameters nor across growth forms.

5.12 Total sugars

hist(in_traitb$T_sugar_per)

hist(log(in_traitb$T_sugar_per+2))

av2<- lmer(T_sugar_per~ diameter_class*growth_form +(1|species),data = in_traitb)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                            Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## diameter_class             0.1360  0.1360     1 26.458  0.2564 0.616788   
## growth_form                5.1785  5.1785     1 21.458  9.7608 0.005038 **
## diameter_class:growth_form 0.0001  0.0001     1 26.458  0.0003 0.987312   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r.squaredGLMM(av2)
##            R2m       R2c
## [1,] 0.2870332 0.7830753
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T_sugar_per ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitb
## 
## REML criterion at convergence: 119.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.57045 -0.16438 -0.05406  0.18881  2.52091 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 1.2076   1.0989  
##  Residual             0.5076   0.7124  
## Number of obs: 40, groups:  species, 22
## 
## Fixed effects:
##                      Estimate Std. Error      df t value Pr(>|t|)   
## (Intercept)            0.4692     0.4329 29.5879   1.084  0.28713   
## diameter_class5.0 cm   0.1650     0.3212 27.0393   0.514  0.61161   
## growth_formTree        1.6788     0.5286 20.8515   3.176  0.00458 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.519       
## grwth_frmTr -0.651  0.103
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## diameter_class 0.1340  0.1340     1 27.039  0.2639 0.611609   
## growth_form    5.1205  5.1205     1 20.852 10.0886 0.004575 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m<- aov(T_sugar_per~growth_form,data=in_traitb)
summary(m)
##             Df Sum Sq Mean Sq F value  Pr(>F)    
## growth_form  1  30.97  30.970   18.36 0.00012 ***
## Residuals   38  64.11   1.687                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lblbTsug<-expression(paste( F["(1,38)"],"   =   18.36"))

b_sugars<- ggplot(data = in_traitb, aes(x = growth_form, y =T_sugar_per)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Total sugars (%)", fill= "Growth form", title = "l")+
  annotate("text", x = 1.3, y = 6, size=5.5, hjust=0.5,label = as.character(lblbMn), parse=TRUE) +
  annotate("text", x = 1.3, y = 5.8, size=5.5, label = "p =  0.0001")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+  theme(legend.position = "none")
b_sugars

More sugars in tree bark than liana bark.

5.13 Silicon

hist(in_traitb$T_Si_g_kg)

hist(log(in_traitb$T_Si_g_kg+2))

av2<- lmer(T_Si_g_kg~ diameter_class*growth_form +(1|species),data = in_traitb)
anova(av2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                             Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class             0.82917 0.82917     1 16.167  1.6328 0.2194
## growth_form                0.12951 0.12951     1 19.974  0.2550 0.6191
## diameter_class:growth_form 0.76069 0.76069     1 16.167  1.4980 0.2385
r.squaredGLMM(av2)
##             R2m       R2c
## [1,] 0.01334417 0.9929902
av3<-update(av2, .~.-diameter_class:growth_form)
summary(av3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T_Si_g_kg ~ diameter_class + growth_form + (1 | species)
##    Data: in_traitb
## 
## REML criterion at convergence: 197.1
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.79741 -0.31800 -0.02842  0.29826  1.89585 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 71.1129  8.4328  
##  Residual              0.5214  0.7221  
## Number of obs: 40, groups:  species, 22
## 
## Fixed effects:
##                      Estimate Std. Error      df t value Pr(>|t|)
## (Intercept)            3.1539     2.5611 20.3284   1.231    0.232
## diameter_class5.0 cm   0.5269     0.3676 17.1663   1.433    0.170
## growth_formTree        1.9305     3.6047 19.9471   0.536    0.598
## 
## Correlation of Fixed Effects:
##             (Intr) d_5.0c
## dmtr_cl5.0c -0.100       
## grwth_frmTr -0.705  0.017
anova(av3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                 Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## diameter_class 1.07096 1.07096     1 17.166  2.0539 0.1698
## growth_form    0.14956 0.14956     1 19.947  0.2868 0.5982
m<- aov(T_Si_g_kg~growth_form,data=in_traitb)
summary(m)
##             Df Sum Sq Mean Sq F value Pr(>F)
## growth_form  1    4.3    4.32   0.075  0.785
## Residuals   38 2182.8   57.44
b_silicon<- ggplot(data = in_traitb, aes(x = growth_form, y =T_sugar_per)) +
  geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
  labs(x= "Growth form", y= "Silicon (g/kg)", fill= "Growth form", title = "m")+
  theme_bw()+
  theme(text = element_text(size = 14,face="bold"), 
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        axis.text = element_text(size=12, face= "bold"))+  theme(legend.position = "none")
b_silicon

No differences between diameters nor across growth forms.

library(patchwork)
b_intrait_plot<- b_carb+b_nit+b_cd+b_cel+b_adl+b_hemc_plot+b_cal+b_pot+b_mag+b_mang+b_ph+b_sugars+b_silicon
#b_intrait_plot

## Initial bark traits across different wood tissue for lianas and trees. (a) Carbon, (b) Nitrogen, (c) condensed tannins (d) cellulose, (e) lignin, (f) Hemicellulose, (g) Calcium, (h) Potassium, (i) Magnesium, (j) Manganese, (k) Phosphorus, (l) Total sugars, (m) Silicon.
ggsave(filename="Figure S4.png", plot=b_intrait_plot, device="png",
       path=path, height=12, width=15, units="in", dpi=500)

Initial traits do not vary between the diameter classes so we can instead use the overall average mean per species.

lev<- c("Calamus henryanus","Ayenia grandifolia","Celastrus sp1","Iodes vitiginea","Celastrus sp2","Cheniella touranensis","Piper flaviflorum",
"Bridelia stipularis","Cayratia trifolia","Ventilago leiocarpa","Senegalia pruinescens","Urceola rosea","Kleinhovia hospita","Macaranga denticulata","Thyrsostachys siamensis","Bauhinia purpurea",
"Toona ciliata","Piper umbellatum","Cunninghamia lanceolata","Tectona grandis","Ficus altissima","Eucalyptus citriodora","Shorea assamica","Mesua ferrea")

in_trait$species<- factor(in_trait$species,levels = lev)
levels(in_trait$species)
##  [1] "Calamus henryanus"       "Ayenia grandifolia"     
##  [3] "Celastrus sp1"           "Iodes vitiginea"        
##  [5] "Celastrus sp2"           "Cheniella touranensis"  
##  [7] "Piper flaviflorum"       "Bridelia stipularis"    
##  [9] "Cayratia trifolia"       "Ventilago leiocarpa"    
## [11] "Senegalia pruinescens"   "Urceola rosea"          
## [13] "Kleinhovia hospita"      "Macaranga denticulata"  
## [15] "Thyrsostachys siamensis" "Bauhinia purpurea"      
## [17] "Toona ciliata"           "Piper umbellatum"       
## [19] "Cunninghamia lanceolata" "Tectona grandis"        
## [21] "Ficus altissima"         "Eucalyptus citriodora"  
## [23] "Shorea assamica"         "Mesua ferrea"
df<-in_trait %>% arrange(factor(species, levels = lev))

mean_trait<- in_trait %>% group_by(species, wood_bark)%>% 
summarise(Carbon= mean(T_C,na.rm = TRUE)/10,                                                          cellulose=mean(cellulose,na.rm=TRUE),
Nitrogen= mean(T_N/10,na.rm = TRUE)/10,
hemicellulose= mean(hemicellulose,na.rm=TRUE),
Tannins= mean(Tannins,na.rm = TRUE),                                                                    ADL=mean(ADL_per,na.rm=TRUE),
Calcium=mean(Ca_g_kg, na.rm=TRUE),
Potassium= mean(K_g_kg, na.rm=TRUE),
magesium= mean(Mg_g_kg, na.rm=TRUE),
Manganese= mean(Mn_mg_kg, na.rm=TRUE),
Phosphorous= mean(P_g_kg, narm=TRUE),
sugars= mean(T_sugar_per, narm=TRUE),
Silicon= mean(T_Si_g_kg, na.rm= TRUE)
)


mean_trait$species<- as.factor(mean_trait$species)
mean_trait$species<- factor(mean_trait$species, levels = lev)
df <- mean_trait[order(levels(mean_trait$species)),]

levels(in_trait$species)
##  [1] "Calamus henryanus"       "Ayenia grandifolia"     
##  [3] "Celastrus sp1"           "Iodes vitiginea"        
##  [5] "Celastrus sp2"           "Cheniella touranensis"  
##  [7] "Piper flaviflorum"       "Bridelia stipularis"    
##  [9] "Cayratia trifolia"       "Ventilago leiocarpa"    
## [11] "Senegalia pruinescens"   "Urceola rosea"          
## [13] "Kleinhovia hospita"      "Macaranga denticulata"  
## [15] "Thyrsostachys siamensis" "Bauhinia purpurea"      
## [17] "Toona ciliata"           "Piper umbellatum"       
## [19] "Cunninghamia lanceolata" "Tectona grandis"        
## [21] "Ficus altissima"         "Eucalyptus citriodora"  
## [23] "Shorea assamica"         "Mesua ferrea"
# function to calculate mean plus/minus std error
meanse <- function(x, ...){
  mean1 <-   signif(round(mean(x, na.rm=T),2), 3)   #calculate mean and round
  se1 <- signif(round(sd(x, na.rm=T)/sqrt(sum(!is.na(x))), 2),2) # std error - round adding zeros
  out <- paste(mean1, "$\\pm$", se1)  # paste together mean plus/minus and standard error
  if (str_detect(out,"NA")) {out="NA"}   # if missing do not add plusminus
  return(out)
}
in_trait$Nitrogen<- in_trait$T_N/10
in_trait$Carbon<- in_trait$T_C/10
# select columns
# then form grouping variables
# then calculate summary statistics using function
t1 <- in_trait %>% dplyr::select(c(species, growth_form, wood_bark,Carbon, Nitrogen, Tannins, ADL_per, cellulose, hemicellulose, Ca_g_kg, K_g_kg,Mg_g_kg, Mn_mg_kg,P_g_kg,T_sugar_per,T_Si_g_kg)) %>%  
  group_by(species, growth_form, wood_bark) %>%       
  summarise_all(.funs = meanse)  

t1b<-t1 %>% arrange(factor(species, levels = lev))
library(readr) # reading in the data
library(kableExtra)  # make HTML tables
library(tidyverse)  # for stacking and selecting
library(dplyr)
library(tidyr)

traits_wide <- pivot_wider(mean_trait, names_from = c(wood_bark), values_from = c(Carbon:Silicon))
traits_wide
## # A tibble: 24 × 27
## # Groups:   species [24]
##    species   Carbon_wood Carbon_bark cellulose_wood cellulose_bark Nitrogen_wood
##    <fct>           <dbl>       <dbl>          <dbl>          <dbl>         <dbl>
##  1 Calamus …        48.0        NA             52.6           NA          0.0571
##  2 Ayenia g…        47.6        41.6           46.6           36.9        0.0678
##  3 Celastru…        45.7        45.4           39.4           27.9        0.0857
##  4 Iodes vi…        46.5        44.8           46.4           38.9        0.0939
##  5 Celastru…        46.0        44.4           44.3           19.8        0.063 
##  6 Cheniell…        45.4        40.2           49.1           47.4        0.0762
##  7 Piper fl…        44.2        42.2           40.7           33.7        0.104 
##  8 Bridelia…        47.6        46.2           50.4           46.7        0.0529
##  9 Cayratia…        46.7        49.6           45.3           38.4        0.107 
## 10 Ventilag…        48.0        44.0           46.3           28.9        0.0677
## # ℹ 14 more rows
## # ℹ 21 more variables: Nitrogen_bark <dbl>, hemicellulose_wood <dbl>,
## #   hemicellulose_bark <dbl>, Tannins_wood <dbl>, Tannins_bark <dbl>,
## #   ADL_wood <dbl>, ADL_bark <dbl>, Calcium_wood <dbl>, Calcium_bark <dbl>,
## #   Potassium_wood <dbl>, Potassium_bark <dbl>, magesium_wood <dbl>,
## #   magesium_bark <dbl>, Manganese_wood <dbl>, Manganese_bark <dbl>,
## #   Phosphorous_wood <dbl>, Phosphorous_bark <dbl>, sugars_wood <dbl>, …

The initial traits are presented in the Table S1 as the means for each species. Since there was no significance difference between the traits across diameters, then the means for each trait is the mean of both diameters values.

6 Join intial traits to the datasheet file

in_dat2<- in_dat%>% left_join(traits_wide, by= c("species"))
in_datb<- in_dat2%>% left_join(data_density, by= c("species","growth_form"))
head(in_datb)
##   species_label                 species family   Tag growth_form
## 1           F58 Cunninghamia lanceolata   <NA> T0561       Trees
## 2           F58 Cunninghamia lanceolata   <NA> T0573       Trees
## 3           F58 Cunninghamia lanceolata   <NA> T0577       Trees
## 4           F58 Cunninghamia lanceolata   <NA> T0565       Trees
## 5           F58 Cunninghamia lanceolata   <NA> T0571       Trees
## 6           F58 Cunninghamia lanceolata   <NA> T0567       Trees
##              mesh_size diameter_class diameter_1.size_cm diameter_2.size_cm
## 1 Invertebrates access         5.0 cm                3.2                3.2
## 2 Invertebrates access         5.0 cm                3.3                3.3
## 3 Invertebrates access         5.0 cm                3.6                3.8
## 4 Invertebrates access         5.0 cm                3.6                3.7
## 5 Invertebrates access         5.0 cm                2.9                3.1
## 6 Invertebrates access         5.0 cm                2.7                2.9
##   diameter_3.size_cm Bark.thickness_1._mm Bark.thickness_2._mm
## 1                3.1                 0.97                 0.48
## 2                3.5                 1.09                 1.24
## 3                3.7                 2.64                 1.71
## 4                3.8                 1.80                 1.86
## 5                3.0                 1.31                 6.04
## 6                3.1                 1.38                 1.14
##   Bark.thickness_3._mm Bark.thickness_4_mm length_cm Wet_weight_of_wood_block_g
## 1                 0.99                0.66      19.5                     150.78
## 2                 0.89                1.08      20.0                     161.74
## 3                 2.37                2.19      20.5                     205.59
## 4                 1.36                1.15      20.5                     207.58
## 5                 0.78                0.84      19.5                     135.11
## 6                 1.31                1.12      20.5                     127.85
##   Wet_weight_of_small_pieces_of_wood_g Volume_of_wet_small_pieces.g
## 1                                14.18                        15.84
## 2                                17.40                        20.15
## 3                                22.80                        25.52
## 4                                21.16                        23.90
## 5                                13.36                        15.82
## 6                                16.38                        19.38
##   Dry_weight_of_small_wood_blocks_g Dry_volume_of_small_wood_blocks.g
## 1                              8.14                             13.79
## 2                             10.09                             17.52
## 3                             12.06                             21.43
## 4                             12.04                             20.90
## 5                              8.49                             14.21
## 6                              9.31                             16.83
##   av_bark_thickness   density av_wd_diameter wd_under_bark bark_wd_ratio
## 1            0.7750 0.5902828       3.166667      3.089167     0.5017534
## 2            1.0750 0.5759132       3.366667      3.259167     0.6596778
## 3            2.2275 0.5627625       3.766667      3.543917     1.2570837
## 4            1.5425 0.5760766       3.733333      3.579083     0.8619525
## 5            2.2425 0.5974666       3.066667      2.842417     1.5778827
## 6            1.2375 0.5531788       2.966667      2.842917     0.8705848
##   initial_mass Carbon_wood Carbon_bark cellulose_wood cellulose_bark
## 1     86.55495      50.475        49.8        43.3975          41.68
## 2     93.79061      50.475        49.8        43.3975          41.68
## 3    108.74629      50.475        49.8        43.3975          41.68
## 4    118.11263      50.475        49.8        43.3975          41.68
## 5     85.85957      50.475        49.8        43.3975          41.68
## 6     72.66688      50.475        49.8        43.3975          41.68
##   Nitrogen_wood Nitrogen_bark hemicellulose_wood hemicellulose_bark
## 1       0.02845        0.0643            11.6775                9.1
## 2       0.02845        0.0643            11.6775                9.1
## 3       0.02845        0.0643            11.6775                9.1
## 4       0.02845        0.0643            11.6775                9.1
## 5       0.02845        0.0643            11.6775                9.1
## 6       0.02845        0.0643            11.6775                9.1
##   Tannins_wood Tannins_bark ADL_wood ADL_bark Calcium_wood Calcium_bark
## 1       0.4875         1.02  35.6825    30.51          5.1        12.84
## 2       0.4875         1.02  35.6825    30.51          5.1        12.84
## 3       0.4875         1.02  35.6825    30.51          5.1        12.84
## 4       0.4875         1.02  35.6825    30.51          5.1        12.84
## 5       0.4875         1.02  35.6825    30.51          5.1        12.84
## 6       0.4875         1.02  35.6825    30.51          5.1        12.84
##   Potassium_wood Potassium_bark magesium_wood magesium_bark Manganese_wood
## 1          1.865           3.56          0.34          0.79          401.3
## 2          1.865           3.56          0.34          0.79          401.3
## 3          1.865           3.56          0.34          0.79          401.3
## 4          1.865           3.56          0.34          0.79          401.3
## 5          1.865           3.56          0.34          0.79          401.3
## 6          1.865           3.56          0.34          0.79          401.3
##   Manganese_bark Phosphorous_wood Phosphorous_bark sugars_wood sugars_bark
## 1          681.3             0.29             0.66      0.4475        0.99
## 2          681.3             0.29             0.66      0.4475        0.99
## 3          681.3             0.29             0.66      0.4475        0.99
## 4          681.3             0.29             0.66      0.4475        0.99
## 5          681.3             0.29             0.66      0.4475        0.99
## 6          681.3             0.29             0.66      0.4475        0.99
##   Silicon_wood Silicon_bark mean_density density_class
## 1        0.175          2.1    0.5611632        medium
## 2        0.175          2.1    0.5611632        medium
## 3        0.175          2.1    0.5611632        medium
## 4        0.175          2.1    0.5611632        medium
## 5        0.175          2.1    0.5611632        medium
## 6        0.175          2.1    0.5611632        medium

7 Import harvest 1, 2, 3 and 4 (6, 12, 18 and 24 months) data

Here we import data of he remaining logs at retrieval times in the experiment (after 6, 12, 18,and 24 months). Please see below the explanation on the data in each column.

1- Tag – The unique log id number. Id number starting with L is liana log while T is tree log.

2- log_fresh_weight- The fresh mass of the entire wood piece (bark + xylem) at the end of decomposition period.

3- wood_weight- The fresh mass of only the xylem after the bark is removed.

4- Subsection- The number of the 2cm discs that were cut from the retrieved log. For each log we have two discs either 1 or 2.

5- disk_fresh_weight- The fresh mass of the 2 cm disc after decomposition.

6- disk_fresh_volume- The volume of the fresh 2 cm disc.

7- disk_dry_weight- This is the dry mass of the disc after oven drying.

8- disk_dry_volume- The volume of the oven dried 2 cm discs.

9- Bark_fresh_mass_at_harvest- The fresh mass of the bark at harvest (it’s the difference between log_fresh_weight and wood_weight)

10- Bark_fresh_volume_at_harvest- The fresh volume of the bark subsection

11- Dry_bark_mass_at_harvest- The dry mass of the bark subsection after oven drying.

12- Dry_bark_volume_at_harvest- The volume of he dry bark subsections

13- Harvest- The harvest plot number (1-4) from which the wood was taken from.

14- Time_months- The number of months between start of the experiment and the wood retrieval date (6, 12, 18 or 24 months)

15- incubation_time- same as Time_months column

16- block- The replicate plot (block 4) from which the log was incubated at.

For the last harvest (24 months) we separated the wood from the bark for all retrieved samples to get an approximate of the proportions of wood and bark remaining at the end of the experiment.

dat<-read.csv("Experiment2_harvest1-4.csv", header = TRUE, sep = ",", quote = "\"",
               dec = ".", fill = TRUE, comment.char = "",fileEncoding = 'latin1')
summary(dat)
##      date               Tag            log_fresh_weight  wood_weight     
##  Length:2844        Length:2844        Min.   :  0.00   Min.   :  0.000  
##  Class :character   Class :character   1st Qu.: 24.43   1st Qu.:  0.000  
##  Mode  :character   Mode  :character   Median : 48.65   Median :  0.000  
##                                        Mean   : 63.32   Mean   :  9.602  
##                                        3rd Qu.: 87.17   3rd Qu.:  0.000  
##                                        Max.   :386.32   Max.   :182.390  
##                                        NA's   :5                         
##    subsection    disk_fresh_weight disk_fresh_volume disk_dry_weight 
##  Min.   :1.000   Min.   : 0.000    Min.   :  0.00    Min.   : 0.000  
##  1st Qu.:1.000   1st Qu.: 2.303    1st Qu.:  4.97    1st Qu.: 1.370  
##  Median :1.000   Median : 4.635    Median :  9.08    Median : 2.875  
##  Mean   :1.457   Mean   : 6.095    Mean   : 11.53    Mean   : 3.763  
##  3rd Qu.:2.000   3rd Qu.: 8.258    3rd Qu.: 15.36    3rd Qu.: 5.067  
##  Max.   :2.000   Max.   :55.430    Max.   :474.00    Max.   :35.260  
##                  NA's   :6         NA's   :7         NA's   :6       
##  disk_dry_volume   Bark_fresh_mass_at_harvest Bark_fresh_volume_at_harvest
##  Min.   :   0.00   Min.   : 0.000             Min.   : 0.0000             
##  1st Qu.:   4.23   1st Qu.: 0.000             1st Qu.: 0.0000             
##  Median :   7.92   Median : 0.000             Median : 0.0000             
##  Mean   :  11.29   Mean   : 1.076             Mean   : 0.7675             
##  3rd Qu.:  13.54   3rd Qu.: 0.000             3rd Qu.: 0.0000             
##  Max.   :3921.00   Max.   :39.560             Max.   :35.6400             
##  NA's   :9         NA's   :10                 NA's   :4                   
##  Dry_bark_mass_at_harvest Dry_bark_volume_at_harvest    Harvest    
##  Min.   : 0.0000          Min.   : 0.0000            Min.   :1.00  
##  1st Qu.: 0.0000          1st Qu.: 0.0000            1st Qu.:1.00  
##  Median : 0.0000          Median : 0.0000            Median :2.00  
##  Mean   : 0.5704          Mean   : 0.5476            Mean   :2.45  
##  3rd Qu.: 0.0000          3rd Qu.: 0.0000            3rd Qu.:3.00  
##  Max.   :16.7900          Max.   :30.8600            Max.   :4.00  
##  NA's   :5                NA's   :6                  NA's   :4     
##  Time_months        incubation_time     block      
##  Length:2844        Min.   : 6.0    Min.   :1.000  
##  Class :character   1st Qu.: 6.0    1st Qu.:1.000  
##  Mode  :character   Median :12.0    Median :2.000  
##                     Mean   :14.7    Mean   :2.477  
##                     3rd Qu.:18.0    3rd Qu.:3.000  
##                     Max.   :24.0    Max.   :4.000  
##                     NA's   :4       NA's   :4

After harvest, we had two discs for each of the wood. Here we combine them together and find the mean for the two discs and use them to calculate the wood and bark dry mass at harvest time.

dat$Tag<-as.factor(dat$Tag)
dat$block<-as.factor(dat$block)
dat$Harvest<-as.factor(dat$Harvest)
dat$Time_months<-as.factor(dat$Time_months)
dat$incubation_time<-as.factor(dat$incubation_time)
dat$disk_fresh_weight<-as.numeric(dat$disk_fresh_weight)
summary(dat)
##      date                Tag       log_fresh_weight  wood_weight     
##  Length:2844        L0817  :   4   Min.   :  0.00   Min.   :  0.000  
##  Class :character   T0358  :   4   1st Qu.: 24.43   1st Qu.:  0.000  
##  Mode  :character   L1253  :   3   Median : 48.65   Median :  0.000  
##                     L0102  :   2   Mean   : 63.32   Mean   :  9.602  
##                     L0104  :   2   3rd Qu.: 87.17   3rd Qu.:  0.000  
##                     L0105  :   2   Max.   :386.32   Max.   :182.390  
##                     (Other):2827   NA's   :5                         
##    subsection    disk_fresh_weight disk_fresh_volume disk_dry_weight 
##  Min.   :1.000   Min.   : 0.000    Min.   :  0.00    Min.   : 0.000  
##  1st Qu.:1.000   1st Qu.: 2.303    1st Qu.:  4.97    1st Qu.: 1.370  
##  Median :1.000   Median : 4.635    Median :  9.08    Median : 2.875  
##  Mean   :1.457   Mean   : 6.095    Mean   : 11.53    Mean   : 3.763  
##  3rd Qu.:2.000   3rd Qu.: 8.258    3rd Qu.: 15.36    3rd Qu.: 5.067  
##  Max.   :2.000   Max.   :55.430    Max.   :474.00    Max.   :35.260  
##                  NA's   :6         NA's   :7         NA's   :6       
##  disk_dry_volume   Bark_fresh_mass_at_harvest Bark_fresh_volume_at_harvest
##  Min.   :   0.00   Min.   : 0.000             Min.   : 0.0000             
##  1st Qu.:   4.23   1st Qu.: 0.000             1st Qu.: 0.0000             
##  Median :   7.92   Median : 0.000             Median : 0.0000             
##  Mean   :  11.29   Mean   : 1.076             Mean   : 0.7675             
##  3rd Qu.:  13.54   3rd Qu.: 0.000             3rd Qu.: 0.0000             
##  Max.   :3921.00   Max.   :39.560             Max.   :35.6400             
##  NA's   :9         NA's   :10                 NA's   :4                   
##  Dry_bark_mass_at_harvest Dry_bark_volume_at_harvest Harvest       Time_months 
##  Min.   : 0.0000          Min.   : 0.0000            1   :759            :  4  
##  1st Qu.: 0.0000          1st Qu.: 0.0000            2   :715   12 months:715  
##  Median : 0.0000          Median : 0.0000            3   :696   18 months:696  
##  Mean   : 0.5704          Mean   : 0.5476            4   :670   24 months:670  
##  3rd Qu.: 0.0000          3rd Qu.: 0.0000            NA's:  4   6 months :759  
##  Max.   :16.7900          Max.   :30.8600                                      
##  NA's   :5                NA's   :6                                            
##  incubation_time  block    
##  6   :759        1   :727  
##  12  :715        2   :713  
##  18  :696        3   :718  
##  24  :670        4   :682  
##  NA's:  4        NA's:  4  
##                            
## 
library(dplyr)
spc_dens<- dat %>% group_by(block,Tag,Harvest,Time_months,incubation_time)%>% 
  summarize(log_fresh_weight=mean(log_fresh_weight,na.rm = TRUE),
            wood_weight=mean(wood_weight,na.rm = TRUE),
            disk_fresh_weight = mean(disk_fresh_weight,na.rm = TRUE),
            disk_fresh_volume = mean (disk_fresh_volume,na.rm = TRUE),
            disk_dry_weight = mean (disk_dry_weight,na.rm = TRUE),
            disk_dry_volume= mean (disk_dry_volume,na.rm = TRUE),
            Bark_fresh_mass_at_harvest= mean (Bark_fresh_mass_at_harvest,na.rm = TRUE),
            Bark_fresh_volume_at_harvest= mean (Bark_fresh_volume_at_harvest,na.rm = TRUE),
            Dry_bark_mass_at_harvest= mean (Dry_bark_mass_at_harvest,na.rm = TRUE),
            Dry_bark_volume_at_harvest= mean (Dry_bark_volume_at_harvest,na.rm = TRUE),
            )

spc_dens
## # A tibble: 1,535 × 15
## # Groups:   block, Tag, Harvest, Time_months [1,535]
##    block Tag   Harvest Time_months incubation_time log_fresh_weight wood_weight
##    <fct> <fct> <fct>   <fct>       <fct>                      <dbl>       <dbl>
##  1 1     L0102 1       6 months    6                           88.0         0  
##  2 1     L0107 3       18 months   18                           0           0  
##  3 1     L0112 3       18 months   18                          83.2         0  
##  4 1     L0117 2       12 months   12                          50.4         0  
##  5 1     L0120 4       24 months   24                          55.6        27.3
##  6 1     L0126 2       12 months   12                          85.6         0  
##  7 1     L0127 4       24 months   24                          33.4        19.2
##  8 1     L0129 1       6 months    6                           86.2         0  
##  9 1     L0154 3       18 months   18                         188.          0  
## 10 1     L0159 3       18 months   18                          54.0         0  
## # ℹ 1,525 more rows
## # ℹ 8 more variables: disk_fresh_weight <dbl>, disk_fresh_volume <dbl>,
## #   disk_dry_weight <dbl>, disk_dry_volume <dbl>,
## #   Bark_fresh_mass_at_harvest <dbl>, Bark_fresh_volume_at_harvest <dbl>,
## #   Dry_bark_mass_at_harvest <dbl>, Dry_bark_volume_at_harvest <dbl>
spc_dens$bark_mass_fresh<- spc_dens$log_fresh_weight- spc_dens$wood_weight
spc_dens$wood_weight<- spc_dens$wood_weight+0.01
spc_dens$disk_fresh_weight<- spc_dens$disk_fresh_weight+0.1
spc_dens$disk_dry_weight<- spc_dens$disk_dry_weight +0.1

spc_dens$bark_mass_fresh<- spc_dens$bark_mass_fresh+0.1
spc_dens$Bark_fresh_mass_at_harvest<-spc_dens$Bark_fresh_mass_at_harvest+0.1
spc_dens$Dry_bark_mass_at_harvest<- spc_dens$Dry_bark_mass_at_harvest+0.1

Calculate the final dry mass of the logs after decomposition using the same formula from Seibold et al., 2021 shown above.

spc_dens$f_wood_mass<- (spc_dens$wood_weight/spc_dens$disk_fresh_weight)*spc_dens$disk_dry_weight
spc_dens$f_bark_mass<- (spc_dens$bark_mass_fresh/spc_dens$Bark_fresh_mass_at_harvest)*spc_dens$Dry_bark_mass_at_harvest

spc_dens$final_mass<- (spc_dens$log_fresh_weight/spc_dens$disk_fresh_weight)*spc_dens$disk_dry_weight

For some WD, we did not have discs (substantially decomposed) so here we will use the fresh weight log as final mass because this was very trivial.

At each harvest we also took photos of each WD. The following figure S6 Figure S6 Figure S6 represents some examples of liana wood of 4.0 cm diameter (a-d) and 2.5 cm diameter (e-f) at 12 months of decomposition. Inner wood was completely decomposed but the bark was left intact after the incubation period. The wood shown in the figure are from species Ayenia grandifolia (a and e), Senegalia pruinescens (b and d), Urceola rosea (c), and Piper flaviflorum (f).

library(tidyr)
data_new <- spc_dens                                                # Duplicate data
data_new$final_mass[is.na(data_new$final_mass)] <- data_new$log_fresh_weight[is.na(data_new$final_mass)]  # Replace NA values
data_new 
## # A tibble: 1,535 × 19
## # Groups:   block, Tag, Harvest, Time_months [1,535]
##    block Tag   Harvest Time_months incubation_time log_fresh_weight wood_weight
##    <fct> <fct> <fct>   <fct>       <fct>                      <dbl>       <dbl>
##  1 1     L0102 1       6 months    6                           88.0        0.01
##  2 1     L0107 3       18 months   18                           0          0.01
##  3 1     L0112 3       18 months   18                          83.2        0.01
##  4 1     L0117 2       12 months   12                          50.4        0.01
##  5 1     L0120 4       24 months   24                          55.6       27.3 
##  6 1     L0126 2       12 months   12                          85.6        0.01
##  7 1     L0127 4       24 months   24                          33.4       19.2 
##  8 1     L0129 1       6 months    6                           86.2        0.01
##  9 1     L0154 3       18 months   18                         188.         0.01
## 10 1     L0159 3       18 months   18                          54.0        0.01
## # ℹ 1,525 more rows
## # ℹ 12 more variables: disk_fresh_weight <dbl>, disk_fresh_volume <dbl>,
## #   disk_dry_weight <dbl>, disk_dry_volume <dbl>,
## #   Bark_fresh_mass_at_harvest <dbl>, Bark_fresh_volume_at_harvest <dbl>,
## #   Dry_bark_mass_at_harvest <dbl>, Dry_bark_volume_at_harvest <dbl>,
## #   bark_mass_fresh <dbl>, f_wood_mass <dbl>, f_bark_mass <dbl>,
## #   final_mass <dbl>
summary(data_new)
##   block          Tag       Harvest       Time_months  incubation_time
##  1   :385   T0358  :   2   1   :384            :  4   6   :384       
##  2   :383   L0101  :   1   2   :384   12 months:384   12  :384       
##  3   :382   L0102  :   1   3   :385   18 months:385   18  :385       
##  4   :381   L0103  :   1   4   :378   24 months:378   24  :378       
##  NA's:  4   L0104  :   1   NA's:  4   6 months :384   NA's:  4       
##             L0105  :   1                                             
##             (Other):1528                                             
##  log_fresh_weight  wood_weight      disk_fresh_weight disk_fresh_volume
##  Min.   :  0.00   Min.   :  0.010   Min.   : 0.100    Min.   :  0.000  
##  1st Qu.: 17.44   1st Qu.:  0.010   1st Qu.: 1.850    1st Qu.:  4.103  
##  Median : 45.12   Median :  0.010   Median : 4.393    Median :  8.512  
##  Mean   : 58.94   Mean   :  8.878   Mean   : 5.779    Mean   : 10.738  
##  3rd Qu.: 83.33   3rd Qu.:  0.010   3rd Qu.: 7.875    3rd Qu.: 14.734  
##  Max.   :386.32   Max.   :182.400   Max.   :47.450    Max.   :238.445  
##  NA's   :5                          NA's   :5         NA's   :5        
##  disk_dry_weight  disk_dry_volume    Bark_fresh_mass_at_harvest
##  Min.   : 0.100   Min.   :   0.000   Min.   : 0.100            
##  1st Qu.: 1.206   1st Qu.:   3.490   1st Qu.: 0.100            
##  Median : 2.745   Median :   7.425   Median : 0.100            
##  Mean   : 3.612   Mean   :  10.510   Mean   : 1.096            
##  3rd Qu.: 4.942   3rd Qu.:  12.985   3rd Qu.: 0.100            
##  Max.   :28.005   Max.   :1972.775   Max.   :38.000            
##  NA's   :5        NA's   :6                                    
##  Bark_fresh_volume_at_harvest Dry_bark_mass_at_harvest
##  Min.   : 0.0000              Min.   : 0.1000         
##  1st Qu.: 0.0000              1st Qu.: 0.1000         
##  Median : 0.0000              Median : 0.1000         
##  Mean   : 0.7094              Mean   : 0.6305         
##  3rd Qu.: 0.0000              3rd Qu.: 0.1000         
##  Max.   :34.2700              Max.   :15.5450         
##                                                       
##  Dry_bark_volume_at_harvest bark_mass_fresh   f_wood_mass       
##  Min.   : 0.0000            Min.   :-36.67   Min.   :1.335e-03  
##  1st Qu.: 0.0000            1st Qu.: 10.86   1st Qu.:7.370e-03  
##  Median : 0.0000            Median : 36.17   Median :8.344e-03  
##  Mean   : 0.5121            Mean   : 50.14   Mean   :4.021e+00  
##  3rd Qu.: 0.0000            3rd Qu.: 70.35   3rd Qu.:1.000e-02  
##  Max.   :30.3150            Max.   :386.42   Max.   :1.009e+02  
##                             NA's   :5        NA's   :5          
##   f_bark_mass        final_mass    
##  Min.   :-20.200   Min.   :  0.00  
##  1st Qu.:  8.403   1st Qu.: 11.21  
##  Median : 32.120   Median : 28.57  
##  Mean   : 48.395   Mean   : 37.03  
##  3rd Qu.: 67.765   3rd Qu.: 52.43  
##  Max.   :386.420   Max.   :235.69  
##  NA's   :5         NA's   :5
dim(data_new)
## [1] 1535   19
data_new$Harvest<-as.factor(data_new$Harvest)
data_new$block<-as.factor(data_new$block)
summary(data_new)
##   block          Tag       Harvest       Time_months  incubation_time
##  1   :385   T0358  :   2   1   :384            :  4   6   :384       
##  2   :383   L0101  :   1   2   :384   12 months:384   12  :384       
##  3   :382   L0102  :   1   3   :385   18 months:385   18  :385       
##  4   :381   L0103  :   1   4   :378   24 months:378   24  :378       
##  NA's:  4   L0104  :   1   NA's:  4   6 months :384   NA's:  4       
##             L0105  :   1                                             
##             (Other):1528                                             
##  log_fresh_weight  wood_weight      disk_fresh_weight disk_fresh_volume
##  Min.   :  0.00   Min.   :  0.010   Min.   : 0.100    Min.   :  0.000  
##  1st Qu.: 17.44   1st Qu.:  0.010   1st Qu.: 1.850    1st Qu.:  4.103  
##  Median : 45.12   Median :  0.010   Median : 4.393    Median :  8.512  
##  Mean   : 58.94   Mean   :  8.878   Mean   : 5.779    Mean   : 10.738  
##  3rd Qu.: 83.33   3rd Qu.:  0.010   3rd Qu.: 7.875    3rd Qu.: 14.734  
##  Max.   :386.32   Max.   :182.400   Max.   :47.450    Max.   :238.445  
##  NA's   :5                          NA's   :5         NA's   :5        
##  disk_dry_weight  disk_dry_volume    Bark_fresh_mass_at_harvest
##  Min.   : 0.100   Min.   :   0.000   Min.   : 0.100            
##  1st Qu.: 1.206   1st Qu.:   3.490   1st Qu.: 0.100            
##  Median : 2.745   Median :   7.425   Median : 0.100            
##  Mean   : 3.612   Mean   :  10.510   Mean   : 1.096            
##  3rd Qu.: 4.942   3rd Qu.:  12.985   3rd Qu.: 0.100            
##  Max.   :28.005   Max.   :1972.775   Max.   :38.000            
##  NA's   :5        NA's   :6                                    
##  Bark_fresh_volume_at_harvest Dry_bark_mass_at_harvest
##  Min.   : 0.0000              Min.   : 0.1000         
##  1st Qu.: 0.0000              1st Qu.: 0.1000         
##  Median : 0.0000              Median : 0.1000         
##  Mean   : 0.7094              Mean   : 0.6305         
##  3rd Qu.: 0.0000              3rd Qu.: 0.1000         
##  Max.   :34.2700              Max.   :15.5450         
##                                                       
##  Dry_bark_volume_at_harvest bark_mass_fresh   f_wood_mass       
##  Min.   : 0.0000            Min.   :-36.67   Min.   :1.335e-03  
##  1st Qu.: 0.0000            1st Qu.: 10.86   1st Qu.:7.370e-03  
##  Median : 0.0000            Median : 36.17   Median :8.344e-03  
##  Mean   : 0.5121            Mean   : 50.14   Mean   :4.021e+00  
##  3rd Qu.: 0.0000            3rd Qu.: 70.35   3rd Qu.:1.000e-02  
##  Max.   :30.3150            Max.   :386.42   Max.   :1.009e+02  
##                             NA's   :5        NA's   :5          
##   f_bark_mass        final_mass    
##  Min.   :-20.200   Min.   :  0.00  
##  1st Qu.:  8.403   1st Qu.: 11.21  
##  Median : 32.120   Median : 28.57  
##  Mean   : 48.395   Mean   : 37.03  
##  3rd Qu.: 67.765   3rd Qu.: 52.43  
##  Max.   :386.420   Max.   :235.69  
##  NA's   :5         NA's   :5
setdiff(data_new$Tag,in_dat$Tag)
## factor()
## 1534 Levels: L0101 L0102 L0103 L0104 L0105 L0106 L0107 L0108 L0109 ... T1182
setdiff(in_dat$Tag, data_new$Tag)
## [1] T0506 L1060
## 1536 Levels: L0101 L0102 L0103 L0104 L0105 L0106 L0107 L0108 L0109 ... T1182
#summary(data_new)

7.1 Join the harvest data to the initial data file

hav_dat<- in_datb%>% left_join(data_new, by= c("Tag"))
summary(hav_dat)
##  species_label                   species      family          Tag      
##  F57    :  65   Eucalyptus citriodora:  65   NA's:1537   T0358  :   2  
##  1      :  64   Ayenia grandifolia   :  64               L0101  :   1  
##  11     :  64   Bauhinia purpurea    :  64               L0102  :   1  
##  12     :  64   Bridelia stipularis  :  64               L0103  :   1  
##  18     :  64   Calamus henryanus    :  64               L0104  :   1  
##  19     :  64   Cayratia trifolia    :  64               L0105  :   1  
##  (Other):1152   (Other)              :1152               (Other):1530  
##  growth_form                  mesh_size   diameter_class diameter_1.size_cm
##  Lianas:768   Invertebrates access :768   2.5 cm:786     Min.   :1.400     
##  Trees :769   Invertebrates blocked:769   5.0 cm:751     1st Qu.:2.500     
##                                                          Median :3.000     
##                                                          Mean   :3.219     
##                                                          3rd Qu.:3.800     
##                                                          Max.   :8.300     
##                                                          NA's   :1         
##  diameter_2.size_cm diameter_3.size_cm Bark.thickness_1._mm
##  Min.   :1.400      Min.   : 1.300     Min.   :0.120       
##  1st Qu.:2.500      1st Qu.: 2.500     1st Qu.:1.100       
##  Median :3.100      Median : 3.000     Median :1.560       
##  Mean   :3.229      Mean   : 3.267     Mean   :1.868       
##  3rd Qu.:3.800      3rd Qu.: 3.900     3rd Qu.:2.380       
##  Max.   :8.500      Max.   :36.000     Max.   :7.430       
##                                        NA's   :64          
##  Bark.thickness_2._mm Bark.thickness_3._mm Bark.thickness_4_mm   length_cm     
##  Min.   :0.130        Min.   :0.110        Min.   :0.010       Min.   :  1.90  
##  1st Qu.:1.040        1st Qu.:1.060        1st Qu.:1.050       1st Qu.: 19.50  
##  Median :1.500        Median :1.500        Median :1.475       Median : 20.00  
##  Mean   :1.803        Mean   :1.823        Mean   :1.790       Mean   : 20.57  
##  3rd Qu.:2.300        3rd Qu.:2.280        3rd Qu.:2.250       3rd Qu.: 20.50  
##  Max.   :8.600        Max.   :7.880        Max.   :8.130       Max.   :205.00  
##  NA's   :64           NA's   :64           NA's   :65                          
##  Wet_weight_of_wood_block_g Wet_weight_of_small_pieces_of_wood_g
##  Min.   :   25.04           Min.   : 1.45                       
##  1st Qu.:   71.85           1st Qu.: 8.19                       
##  Median :  117.49           Median :13.32                       
##  Mean   :  154.48           Mean   :16.79                       
##  3rd Qu.:  188.28           3rd Qu.:21.98                       
##  Max.   :14550.00           Max.   :80.30                       
##  NA's   :2                                                      
##  Volume_of_wet_small_pieces.g Dry_weight_of_small_wood_blocks_g
##  Min.   :  1.70               Min.   : 1.670                   
##  1st Qu.: 11.02               1st Qu.: 4.840                   
##  Median : 17.20               Median : 7.230                   
##  Mean   : 20.50               Mean   : 8.745                   
##  3rd Qu.: 26.42               3rd Qu.:11.160                   
##  Max.   :151.33               Max.   :42.500                   
##                               NA's   :12                       
##  Dry_volume_of_small_wood_blocks.g av_bark_thickness    density       
##  Min.   : 3.32                     Min.   :0.230     Min.   :0.06948  
##  1st Qu.: 9.39                     1st Qu.:1.104     1st Qu.:0.44807  
##  Median :14.46                     Median :1.516     Median :0.50527  
##  Mean   :16.98                     Mean   :1.822     Mean   :0.52689  
##  3rd Qu.:21.92                     3rd Qu.:2.297     3rd Qu.:0.58071  
##  Max.   :99.71                     Max.   :7.145     Max.   :1.00422  
##  NA's   :12                        NA's   :65        NA's   :12       
##  av_wd_diameter   wd_under_bark    bark_wd_ratio     initial_mass    
##  Min.   : 1.400   Min.   : 1.281   Min.   :0.1698   Min.   :  11.46  
##  1st Qu.: 2.467   1st Qu.: 2.319   1st Qu.:0.7254   1st Qu.:  41.44  
##  Median : 3.067   Median : 2.891   Median :1.0854   Median :  62.69  
##  Mean   : 3.242   Mean   : 3.043   Mean   :1.2171   Mean   :  82.67  
##  3rd Qu.: 3.867   3rd Qu.: 3.578   3rd Qu.:1.5863   3rd Qu.:  97.86  
##  Max.   :14.667   Max.   :14.543   Max.   :3.8754   Max.   :8065.56  
##                   NA's   :65       NA's   :65       NA's   :14       
##   Carbon_wood     Carbon_bark    cellulose_wood  cellulose_bark 
##  Min.   :44.23   Min.   :40.25   Min.   :39.36   Min.   :19.84  
##  1st Qu.:46.50   1st Qu.:43.30   1st Qu.:46.34   1st Qu.:34.08  
##  Median :47.50   Median :44.77   Median :49.51   Median :45.15  
##  Mean   :47.10   Mean   :45.29   Mean   :49.54   Mean   :40.80  
##  3rd Qu.:47.88   3rd Qu.:47.60   3rd Qu.:55.44   3rd Qu.:47.45  
##  Max.   :50.48   Max.   :49.90   Max.   :58.87   Max.   :56.35  
##                  NA's   :128                     NA's   :128    
##  Nitrogen_wood     Nitrogen_bark     hemicellulose_wood hemicellulose_bark
##  Min.   :0.02365   Min.   :0.05325   Min.   :10.14      Min.   : 8.355    
##  1st Qu.:0.05287   1st Qu.:0.07070   1st Qu.:15.65      1st Qu.: 9.930    
##  Median :0.06540   Median :0.08640   Median :17.05      Median :13.075    
##  Mean   :0.07021   Mean   :0.10491   Mean   :16.82      Mean   :13.021    
##  3rd Qu.:0.08220   3rd Qu.:0.13310   3rd Qu.:18.18      3rd Qu.:15.345    
##  Max.   :0.16820   Max.   :0.21495   Max.   :23.69      Max.   :19.290    
##                    NA's   :128                          NA's   :128       
##   Tannins_wood     Tannins_bark      ADL_wood        ADL_bark    
##  Min.   :0.0350   Min.   :0.030   Min.   :14.78   Min.   : 7.57  
##  1st Qu.:0.1000   1st Qu.:0.340   1st Qu.:17.28   1st Qu.:13.40  
##  Median :0.2625   Median :1.020   Median :18.88   Median :19.34  
##  Mean   :0.6439   Mean   :1.480   Mean   :20.30   Mean   :20.73  
##  3rd Qu.:0.6675   3rd Qu.:2.145   3rd Qu.:20.48   3rd Qu.:28.53  
##  Max.   :4.6800   Max.   :5.985   Max.   :35.68   Max.   :37.78  
##                   NA's   :192                     NA's   :128    
##   Calcium_wood     Calcium_bark   Potassium_wood   Potassium_bark  
##  Min.   : 1.965   Min.   : 7.38   Min.   : 1.735   Min.   : 2.335  
##  1st Qu.: 5.100   1st Qu.:21.20   1st Qu.: 3.700   1st Qu.: 4.705  
##  Median :13.127   Median :33.00   Median : 5.345   Median : 6.270  
##  Mean   :14.452   Mean   :35.13   Mean   : 7.236   Mean   : 7.207  
##  3rd Qu.:18.525   3rd Qu.:48.68   3rd Qu.: 9.957   3rd Qu.: 7.975  
##  Max.   :42.422   Max.   :71.70   Max.   :23.517   Max.   :20.383  
##                   NA's   :128                      NA's   :128     
##  magesium_wood    magesium_bark   Manganese_wood   Manganese_bark   
##  Min.   :0.3400   Min.   :0.500   Min.   :  8.70   Min.   :  11.30  
##  1st Qu.:0.6425   1st Qu.:1.175   1st Qu.: 12.18   1st Qu.:  25.25  
##  Median :1.0400   Median :2.100   Median : 18.55   Median :  76.70  
##  Mean   :1.1255   Mean   :1.880   Mean   : 85.38   Mean   : 202.32  
##  3rd Qu.:1.5050   3rd Qu.:2.505   3rd Qu.: 66.08   3rd Qu.: 254.20  
##  Max.   :2.2675   Max.   :3.417   Max.   :586.92   Max.   :1265.15  
##                   NA's   :128                      NA's   :128      
##  Phosphorous_wood Phosphorous_bark  sugars_wood     sugars_bark   
##  Min.   :0.2000   Min.   :0.390    Min.   :0.160   Min.   :0.110  
##  1st Qu.:0.6150   1st Qu.:0.605    1st Qu.:0.420   1st Qu.:0.420  
##  Median :0.8075   Median :0.965    Median :0.865   Median :0.990  
##  Mean   :1.2198   Mean   :1.190    Mean   :1.063   Mean   :1.378  
##  3rd Qu.:1.3467   3rd Qu.:1.590    3rd Qu.:1.252   3rd Qu.:1.900  
##  Max.   :4.0950   Max.   :3.840    Max.   :4.530   Max.   :6.195  
##                   NA's   :128                      NA's   :128    
##   Silicon_wood      Silicon_bark     mean_density    density_class     
##  Min.   : 0.1750   Min.   : 0.340   Min.   :0.4083   Length:1537       
##  1st Qu.: 0.5000   1st Qu.: 0.770   1st Qu.:0.4701   Class :character  
##  Median : 0.5975   Median : 1.655   Median :0.5008   Mode  :character  
##  Mean   : 1.6683   Mean   : 4.443   Mean   :0.5269                     
##  3rd Qu.: 1.3725   3rd Qu.: 2.685   3rd Qu.:0.5612                     
##  Max.   :12.9000   Max.   :31.580   Max.   :0.8475                     
##                    NA's   :128                                         
##   block     Harvest       Time_months  incubation_time log_fresh_weight
##  1   :385   1   :384            :  4   6   :384        Min.   :  0.00  
##  2   :383   2   :384   12 months:384   12  :384        1st Qu.: 17.44  
##  3   :382   3   :385   18 months:385   18  :385        Median : 45.12  
##  4   :381   4   :378   24 months:378   24  :378        Mean   : 58.94  
##  NA's:  6   NA's:  6   6 months :384   NA's:  6        3rd Qu.: 83.33  
##                        NA's     :  2                   Max.   :386.32  
##                                                        NA's   :7       
##   wood_weight      disk_fresh_weight disk_fresh_volume disk_dry_weight 
##  Min.   :  0.010   Min.   : 0.100    Min.   :  0.000   Min.   : 0.100  
##  1st Qu.:  0.010   1st Qu.: 1.850    1st Qu.:  4.103   1st Qu.: 1.206  
##  Median :  0.010   Median : 4.393    Median :  8.512   Median : 2.745  
##  Mean   :  8.878   Mean   : 5.779    Mean   : 10.738   Mean   : 3.612  
##  3rd Qu.:  0.010   3rd Qu.: 7.875    3rd Qu.: 14.734   3rd Qu.: 4.942  
##  Max.   :182.400   Max.   :47.450    Max.   :238.445   Max.   :28.005  
##  NA's   :2         NA's   :7         NA's   :7         NA's   :7       
##  disk_dry_volume    Bark_fresh_mass_at_harvest Bark_fresh_volume_at_harvest
##  Min.   :   0.000   Min.   : 0.100             Min.   : 0.0000             
##  1st Qu.:   3.490   1st Qu.: 0.100             1st Qu.: 0.0000             
##  Median :   7.425   Median : 0.100             Median : 0.0000             
##  Mean   :  10.510   Mean   : 1.096             Mean   : 0.7094             
##  3rd Qu.:  12.985   3rd Qu.: 0.100             3rd Qu.: 0.0000             
##  Max.   :1972.775   Max.   :38.000             Max.   :34.2700             
##  NA's   :8          NA's   :2                  NA's   :2                   
##  Dry_bark_mass_at_harvest Dry_bark_volume_at_harvest bark_mass_fresh 
##  Min.   : 0.1000          Min.   : 0.0000            Min.   :-36.67  
##  1st Qu.: 0.1000          1st Qu.: 0.0000            1st Qu.: 10.86  
##  Median : 0.1000          Median : 0.0000            Median : 36.17  
##  Mean   : 0.6305          Mean   : 0.5121            Mean   : 50.14  
##  3rd Qu.: 0.1000          3rd Qu.: 0.0000            3rd Qu.: 70.35  
##  Max.   :15.5450          Max.   :30.3150            Max.   :386.42  
##  NA's   :2                NA's   :2                  NA's   :7       
##   f_wood_mass         f_bark_mass        final_mass    
##  Min.   :1.335e-03   Min.   :-20.200   Min.   :  0.00  
##  1st Qu.:7.370e-03   1st Qu.:  8.403   1st Qu.: 11.21  
##  Median :8.344e-03   Median : 32.120   Median : 28.57  
##  Mean   :4.021e+00   Mean   : 48.395   Mean   : 37.03  
##  3rd Qu.:1.000e-02   3rd Qu.: 67.765   3rd Qu.: 52.43  
##  Max.   :1.009e+02   Max.   :386.420   Max.   :235.69  
##  NA's   :7           NA's   :7         NA's   :7
hav_datb<- hav_dat %>% drop_na(Harvest)
summary(hav_datb)
##  species_label                   species      family          Tag      
##  F57    :  65   Eucalyptus citriodora:  65   NA's:1531   T0358  :   2  
##  1      :  64   Ayenia grandifolia   :  64               L0101  :   1  
##  11     :  64   Bauhinia purpurea    :  64               L0102  :   1  
##  12     :  64   Bridelia stipularis  :  64               L0103  :   1  
##  18     :  64   Calamus henryanus    :  64               L0104  :   1  
##  19     :  64   Celastrus sp2        :  64               L0105  :   1  
##  (Other):1146   (Other)              :1146               (Other):1524  
##  growth_form                  mesh_size   diameter_class diameter_1.size_cm
##  Lianas:764   Invertebrates access :764   2.5 cm:782     Min.   :1.40      
##  Trees :767   Invertebrates blocked:767   5.0 cm:749     1st Qu.:2.50      
##                                                          Median :3.00      
##                                                          Mean   :3.22      
##                                                          3rd Qu.:3.80      
##                                                          Max.   :8.30      
##                                                          NA's   :1         
##  diameter_2.size_cm diameter_3.size_cm Bark.thickness_1._mm
##  Min.   :1.400      Min.   : 1.300     Min.   :0.120       
##  1st Qu.:2.500      1st Qu.: 2.500     1st Qu.:1.100       
##  Median :3.100      Median : 3.100     Median :1.560       
##  Mean   :3.229      Mean   : 3.267     Mean   :1.868       
##  3rd Qu.:3.800      3rd Qu.: 3.900     3rd Qu.:2.380       
##  Max.   :8.500      Max.   :36.000     Max.   :7.430       
##                                        NA's   :64          
##  Bark.thickness_2._mm Bark.thickness_3._mm Bark.thickness_4_mm   length_cm     
##  Min.   :0.130        Min.   :0.110        Min.   :0.01        Min.   :  1.90  
##  1st Qu.:1.040        1st Qu.:1.070        1st Qu.:1.05        1st Qu.: 19.50  
##  Median :1.500        Median :1.500        Median :1.48        Median : 20.00  
##  Mean   :1.805        Mean   :1.824        Mean   :1.79        Mean   : 20.58  
##  3rd Qu.:2.305        3rd Qu.:2.285        3rd Qu.:2.25        3rd Qu.: 20.50  
##  Max.   :8.600        Max.   :7.880        Max.   :8.13        Max.   :205.00  
##  NA's   :64           NA's   :64           NA's   :65                          
##  Wet_weight_of_wood_block_g Wet_weight_of_small_pieces_of_wood_g
##  Min.   :   25.04           Min.   : 1.45                       
##  1st Qu.:   71.73           1st Qu.: 8.19                       
##  Median :  117.64           Median :13.34                       
##  Mean   :  154.57           Mean   :16.79                       
##  3rd Qu.:  188.16           3rd Qu.:21.98                       
##  Max.   :14550.00           Max.   :80.30                       
##  NA's   :2                                                      
##  Volume_of_wet_small_pieces.g Dry_weight_of_small_wood_blocks_g
##  Min.   :  1.70               Min.   : 1.670                   
##  1st Qu.: 10.99               1st Qu.: 4.840                   
##  Median : 17.24               Median : 7.250                   
##  Mean   : 20.50               Mean   : 8.752                   
##  3rd Qu.: 26.41               3rd Qu.:11.165                   
##  Max.   :151.33               Max.   :42.500                   
##                               NA's   :12                       
##  Dry_volume_of_small_wood_blocks.g av_bark_thickness    density       
##  Min.   : 3.32                     Min.   :0.230     Min.   :0.06948  
##  1st Qu.: 9.38                     1st Qu.:1.105     1st Qu.:0.44810  
##  Median :14.47                     Median :1.518     Median :0.50542  
##  Mean   :16.99                     Mean   :1.822     Mean   :0.52695  
##  3rd Qu.:21.92                     3rd Qu.:2.297     3rd Qu.:0.58077  
##  Max.   :99.71                     Max.   :7.145     Max.   :1.00422  
##  NA's   :12                        NA's   :65        NA's   :12       
##  av_wd_diameter   wd_under_bark    bark_wd_ratio     initial_mass    
##  Min.   : 1.400   Min.   : 1.281   Min.   :0.1698   Min.   :  11.46  
##  1st Qu.: 2.467   1st Qu.: 2.318   1st Qu.:0.7254   1st Qu.:  41.43  
##  Median : 3.067   Median : 2.893   Median :1.0868   Median :  63.00  
##  Mean   : 3.242   Mean   : 3.043   Mean   :1.2175   Mean   :  82.77  
##  3rd Qu.: 3.867   3rd Qu.: 3.577   3rd Qu.:1.5837   3rd Qu.:  98.15  
##  Max.   :14.667   Max.   :14.543   Max.   :3.8754   Max.   :8065.56  
##                   NA's   :65       NA's   :65       NA's   :14       
##   Carbon_wood     Carbon_bark    cellulose_wood  cellulose_bark 
##  Min.   :44.23   Min.   :40.25   Min.   :39.36   Min.   :19.84  
##  1st Qu.:46.50   1st Qu.:43.30   1st Qu.:46.34   1st Qu.:34.08  
##  Median :47.60   Median :44.77   Median :49.51   Median :45.15  
##  Mean   :47.10   Mean   :45.27   Mean   :49.56   Mean   :40.81  
##  3rd Qu.:47.91   3rd Qu.:47.60   3rd Qu.:55.44   3rd Qu.:47.45  
##  Max.   :50.48   Max.   :49.90   Max.   :58.87   Max.   :56.35  
##                  NA's   :128                     NA's   :128    
##  Nitrogen_wood     Nitrogen_bark     hemicellulose_wood hemicellulose_bark
##  Min.   :0.02365   Min.   :0.05325   Min.   :10.14      Min.   : 8.355    
##  1st Qu.:0.05287   1st Qu.:0.07070   1st Qu.:15.65      1st Qu.: 9.930    
##  Median :0.06540   Median :0.09260   Median :17.05      Median :13.075    
##  Mean   :0.07015   Mean   :0.10493   Mean   :16.82      Mean   :13.021    
##  3rd Qu.:0.08220   3rd Qu.:0.13310   3rd Qu.:18.44      3rd Qu.:15.345    
##  Max.   :0.16820   Max.   :0.21495   Max.   :23.69      Max.   :19.290    
##                    NA's   :128                          NA's   :128       
##   Tannins_wood     Tannins_bark      ADL_wood        ADL_bark    
##  Min.   :0.0350   Min.   :0.030   Min.   :14.78   Min.   : 7.57  
##  1st Qu.:0.1000   1st Qu.:0.340   1st Qu.:17.17   1st Qu.:13.40  
##  Median :0.2625   Median :1.020   Median :18.88   Median :19.34  
##  Mean   :0.6439   Mean   :1.483   Mean   :20.29   Mean   :20.69  
##  3rd Qu.:0.6238   3rd Qu.:2.145   3rd Qu.:20.48   3rd Qu.:28.53  
##  Max.   :4.6800   Max.   :5.985   Max.   :35.68   Max.   :37.78  
##                   NA's   :192                     NA's   :128    
##   Calcium_wood     Calcium_bark   Potassium_wood   Potassium_bark  
##  Min.   : 1.965   Min.   : 7.38   Min.   : 1.735   Min.   : 2.335  
##  1st Qu.: 4.840   1st Qu.:21.20   1st Qu.: 3.933   1st Qu.: 4.705  
##  Median :13.127   Median :34.24   Median : 5.345   Median : 6.305  
##  Mean   :14.438   Mean   :35.20   Mean   : 7.220   Mean   : 7.208  
##  3rd Qu.:18.525   3rd Qu.:48.68   3rd Qu.: 9.957   3rd Qu.: 7.975  
##  Max.   :42.422   Max.   :71.70   Max.   :23.517   Max.   :20.383  
##                   NA's   :128                      NA's   :128     
##  magesium_wood    magesium_bark   Manganese_wood   Manganese_bark   
##  Min.   :0.3400   Min.   :0.500   Min.   :  8.70   Min.   :  11.30  
##  1st Qu.:0.6425   1st Qu.:1.175   1st Qu.: 12.18   1st Qu.:  25.25  
##  Median :1.0400   Median :2.100   Median : 16.82   Median :  76.70  
##  Mean   :1.1245   Mean   :1.882   Mean   : 85.39   Mean   : 202.51  
##  3rd Qu.:1.5050   3rd Qu.:2.505   3rd Qu.: 66.08   3rd Qu.: 254.20  
##  Max.   :2.2675   Max.   :3.417   Max.   :586.92   Max.   :1265.15  
##                   NA's   :128                      NA's   :128      
##  Phosphorous_wood Phosphorous_bark  sugars_wood      sugars_bark   
##  Min.   :0.2000   Min.   :0.390    Min.   :0.1600   Min.   :0.110  
##  1st Qu.:0.6150   1st Qu.:0.605    1st Qu.:0.3937   1st Qu.:0.420  
##  Median :0.8075   Median :0.965    Median :0.9200   Median :0.990  
##  Mean   :1.2183   Mean   :1.191    Mean   :1.0647   Mean   :1.380  
##  3rd Qu.:1.3467   3rd Qu.:1.590    3rd Qu.:1.4975   3rd Qu.:1.900  
##  Max.   :4.0950   Max.   :3.840    Max.   :4.5300   Max.   :6.195  
##                   NA's   :128                       NA's   :128    
##   Silicon_wood      Silicon_bark     mean_density    density_class      block  
##  Min.   : 0.1750   Min.   : 0.340   Min.   :0.4083   Length:1531        1:385  
##  1st Qu.: 0.5000   1st Qu.: 0.770   1st Qu.:0.4657   Class :character   2:383  
##  Median : 0.5975   Median : 1.580   Median :0.5008   Mode  :character   3:382  
##  Mean   : 1.6684   Mean   : 4.436   Mean   :0.5269                      4:381  
##  3rd Qu.: 1.3725   3rd Qu.: 2.685   3rd Qu.:0.5580                             
##  Max.   :12.9000   Max.   :31.580   Max.   :0.8475                             
##                    NA's   :128                                                 
##  Harvest    Time_months  incubation_time log_fresh_weight  wood_weight     
##  1:384            :  0   6 :384          Min.   :  0.00   Min.   :  0.010  
##  2:384   12 months:384   12:384          1st Qu.: 17.87   1st Qu.:  0.010  
##  3:385   18 months:385   18:385          Median : 45.30   Median :  0.010  
##  4:378   24 months:378   24:378          Mean   : 59.10   Mean   :  8.901  
##          6 months :384                   3rd Qu.: 83.49   3rd Qu.:  0.010  
##                                          Max.   :386.32   Max.   :182.400  
##                                          NA's   :5                         
##  disk_fresh_weight disk_fresh_volume disk_dry_weight  disk_dry_volume   
##  Min.   : 0.100    Min.   :  0.000   Min.   : 0.100   Min.   :   0.000  
##  1st Qu.: 1.881    1st Qu.:  4.206   1st Qu.: 1.218   1st Qu.:   3.520  
##  Median : 4.410    Median :  8.523   Median : 2.745   Median :   7.425  
##  Mean   : 5.793    Mean   : 10.767   Mean   : 3.621   Mean   :  10.538  
##  3rd Qu.: 7.875    3rd Qu.: 14.773   3rd Qu.: 4.945   3rd Qu.:  13.010  
##  Max.   :47.450    Max.   :238.445   Max.   :28.005   Max.   :1972.775  
##  NA's   :5         NA's   :5         NA's   :5        NA's   :6         
##  Bark_fresh_mass_at_harvest Bark_fresh_volume_at_harvest
##  Min.   : 0.100             Min.   : 0.0000             
##  1st Qu.: 0.100             1st Qu.: 0.0000             
##  Median : 0.100             Median : 0.0000             
##  Mean   : 1.099             Mean   : 0.7113             
##  3rd Qu.: 0.100             3rd Qu.: 0.0000             
##  Max.   :38.000             Max.   :34.2700             
##                                                         
##  Dry_bark_mass_at_harvest Dry_bark_volume_at_harvest bark_mass_fresh 
##  Min.   : 0.1000          Min.   : 0.0000            Min.   :-36.67  
##  1st Qu.: 0.1000          1st Qu.: 0.0000            1st Qu.: 10.92  
##  Median : 0.1000          Median : 0.0000            Median : 36.24  
##  Mean   : 0.6319          Mean   : 0.5135            Mean   : 50.28  
##  3rd Qu.: 0.1000          3rd Qu.: 0.0000            3rd Qu.: 70.51  
##  Max.   :15.5450          Max.   :30.3150            Max.   :386.42  
##                                                      NA's   :5       
##   f_wood_mass         f_bark_mass        final_mass    
##  Min.   :1.335e-03   Min.   :-20.200   Min.   :  0.00  
##  1st Qu.:7.362e-03   1st Qu.:  8.466   1st Qu.: 11.37  
##  Median :8.342e-03   Median : 32.355   Median : 28.64  
##  Mean   :4.032e+00   Mean   : 48.522   Mean   : 37.13  
##  3rd Qu.:1.000e-02   3rd Qu.: 67.785   3rd Qu.: 52.45  
##  Max.   :1.009e+02   Max.   :386.420   Max.   :235.69  
##  NA's   :5           NA's   :5         NA's   :5
summary(hav_datb$species) 
##      Ayenia grandifolia       Bauhinia purpurea     Bridelia stipularis 
##                      64                      64                      64 
##       Calamus henryanus       Cayratia trifolia           Celastrus sp1 
##                      64                      62                      63 
##           Celastrus sp2   Cheniella touranensis Cunninghamia lanceolata 
##                      64                      64                      63 
##   Eucalyptus citriodora         Ficus altissima         Iodes vitiginea 
##                      65                      64                      64 
##      Kleinhovia hospita   Macaranga denticulata            Mesua ferrea 
##                      64                      64                      64 
##       Piper flaviflorum        Piper umbellatum   Senegalia pruinescens 
##                      63                      64                      64 
##         Shorea assamica         Tectona grandis Thyrsostachys siamensis 
##                      63                      64                      64 
##           Toona ciliata           Urceola rosea     Ventilago leiocarpa 
##                      64                      64                      64
hav_datb$mesh_size<- as.factor(hav_datb$mesh_size)
hav_datb$Harvest<- as.factor(hav_datb$Harvest)
hav_datb$Time_months<- as.factor(hav_datb$Time_months)
hav_datb$incubation_time<- as.factor(hav_datb$incubation_time)

Calculate percentage mass loss percentage mass loss was calculated using the equation mentioned above and used here as per_ml:

hav_datb$per_ml<- ((hav_datb$initial_mass-hav_datb$final_mass)/hav_datb$initial_mass)*100
summary(hav_datb)
##  species_label                   species      family          Tag      
##  F57    :  65   Eucalyptus citriodora:  65   NA's:1531   T0358  :   2  
##  1      :  64   Ayenia grandifolia   :  64               L0101  :   1  
##  11     :  64   Bauhinia purpurea    :  64               L0102  :   1  
##  12     :  64   Bridelia stipularis  :  64               L0103  :   1  
##  18     :  64   Calamus henryanus    :  64               L0104  :   1  
##  19     :  64   Celastrus sp2        :  64               L0105  :   1  
##  (Other):1146   (Other)              :1146               (Other):1524  
##  growth_form                  mesh_size   diameter_class diameter_1.size_cm
##  Lianas:764   Invertebrates access :764   2.5 cm:782     Min.   :1.40      
##  Trees :767   Invertebrates blocked:767   5.0 cm:749     1st Qu.:2.50      
##                                                          Median :3.00      
##                                                          Mean   :3.22      
##                                                          3rd Qu.:3.80      
##                                                          Max.   :8.30      
##                                                          NA's   :1         
##  diameter_2.size_cm diameter_3.size_cm Bark.thickness_1._mm
##  Min.   :1.400      Min.   : 1.300     Min.   :0.120       
##  1st Qu.:2.500      1st Qu.: 2.500     1st Qu.:1.100       
##  Median :3.100      Median : 3.100     Median :1.560       
##  Mean   :3.229      Mean   : 3.267     Mean   :1.868       
##  3rd Qu.:3.800      3rd Qu.: 3.900     3rd Qu.:2.380       
##  Max.   :8.500      Max.   :36.000     Max.   :7.430       
##                                        NA's   :64          
##  Bark.thickness_2._mm Bark.thickness_3._mm Bark.thickness_4_mm   length_cm     
##  Min.   :0.130        Min.   :0.110        Min.   :0.01        Min.   :  1.90  
##  1st Qu.:1.040        1st Qu.:1.070        1st Qu.:1.05        1st Qu.: 19.50  
##  Median :1.500        Median :1.500        Median :1.48        Median : 20.00  
##  Mean   :1.805        Mean   :1.824        Mean   :1.79        Mean   : 20.58  
##  3rd Qu.:2.305        3rd Qu.:2.285        3rd Qu.:2.25        3rd Qu.: 20.50  
##  Max.   :8.600        Max.   :7.880        Max.   :8.13        Max.   :205.00  
##  NA's   :64           NA's   :64           NA's   :65                          
##  Wet_weight_of_wood_block_g Wet_weight_of_small_pieces_of_wood_g
##  Min.   :   25.04           Min.   : 1.45                       
##  1st Qu.:   71.73           1st Qu.: 8.19                       
##  Median :  117.64           Median :13.34                       
##  Mean   :  154.57           Mean   :16.79                       
##  3rd Qu.:  188.16           3rd Qu.:21.98                       
##  Max.   :14550.00           Max.   :80.30                       
##  NA's   :2                                                      
##  Volume_of_wet_small_pieces.g Dry_weight_of_small_wood_blocks_g
##  Min.   :  1.70               Min.   : 1.670                   
##  1st Qu.: 10.99               1st Qu.: 4.840                   
##  Median : 17.24               Median : 7.250                   
##  Mean   : 20.50               Mean   : 8.752                   
##  3rd Qu.: 26.41               3rd Qu.:11.165                   
##  Max.   :151.33               Max.   :42.500                   
##                               NA's   :12                       
##  Dry_volume_of_small_wood_blocks.g av_bark_thickness    density       
##  Min.   : 3.32                     Min.   :0.230     Min.   :0.06948  
##  1st Qu.: 9.38                     1st Qu.:1.105     1st Qu.:0.44810  
##  Median :14.47                     Median :1.518     Median :0.50542  
##  Mean   :16.99                     Mean   :1.822     Mean   :0.52695  
##  3rd Qu.:21.92                     3rd Qu.:2.297     3rd Qu.:0.58077  
##  Max.   :99.71                     Max.   :7.145     Max.   :1.00422  
##  NA's   :12                        NA's   :65        NA's   :12       
##  av_wd_diameter   wd_under_bark    bark_wd_ratio     initial_mass    
##  Min.   : 1.400   Min.   : 1.281   Min.   :0.1698   Min.   :  11.46  
##  1st Qu.: 2.467   1st Qu.: 2.318   1st Qu.:0.7254   1st Qu.:  41.43  
##  Median : 3.067   Median : 2.893   Median :1.0868   Median :  63.00  
##  Mean   : 3.242   Mean   : 3.043   Mean   :1.2175   Mean   :  82.77  
##  3rd Qu.: 3.867   3rd Qu.: 3.577   3rd Qu.:1.5837   3rd Qu.:  98.15  
##  Max.   :14.667   Max.   :14.543   Max.   :3.8754   Max.   :8065.56  
##                   NA's   :65       NA's   :65       NA's   :14       
##   Carbon_wood     Carbon_bark    cellulose_wood  cellulose_bark 
##  Min.   :44.23   Min.   :40.25   Min.   :39.36   Min.   :19.84  
##  1st Qu.:46.50   1st Qu.:43.30   1st Qu.:46.34   1st Qu.:34.08  
##  Median :47.60   Median :44.77   Median :49.51   Median :45.15  
##  Mean   :47.10   Mean   :45.27   Mean   :49.56   Mean   :40.81  
##  3rd Qu.:47.91   3rd Qu.:47.60   3rd Qu.:55.44   3rd Qu.:47.45  
##  Max.   :50.48   Max.   :49.90   Max.   :58.87   Max.   :56.35  
##                  NA's   :128                     NA's   :128    
##  Nitrogen_wood     Nitrogen_bark     hemicellulose_wood hemicellulose_bark
##  Min.   :0.02365   Min.   :0.05325   Min.   :10.14      Min.   : 8.355    
##  1st Qu.:0.05287   1st Qu.:0.07070   1st Qu.:15.65      1st Qu.: 9.930    
##  Median :0.06540   Median :0.09260   Median :17.05      Median :13.075    
##  Mean   :0.07015   Mean   :0.10493   Mean   :16.82      Mean   :13.021    
##  3rd Qu.:0.08220   3rd Qu.:0.13310   3rd Qu.:18.44      3rd Qu.:15.345    
##  Max.   :0.16820   Max.   :0.21495   Max.   :23.69      Max.   :19.290    
##                    NA's   :128                          NA's   :128       
##   Tannins_wood     Tannins_bark      ADL_wood        ADL_bark    
##  Min.   :0.0350   Min.   :0.030   Min.   :14.78   Min.   : 7.57  
##  1st Qu.:0.1000   1st Qu.:0.340   1st Qu.:17.17   1st Qu.:13.40  
##  Median :0.2625   Median :1.020   Median :18.88   Median :19.34  
##  Mean   :0.6439   Mean   :1.483   Mean   :20.29   Mean   :20.69  
##  3rd Qu.:0.6238   3rd Qu.:2.145   3rd Qu.:20.48   3rd Qu.:28.53  
##  Max.   :4.6800   Max.   :5.985   Max.   :35.68   Max.   :37.78  
##                   NA's   :192                     NA's   :128    
##   Calcium_wood     Calcium_bark   Potassium_wood   Potassium_bark  
##  Min.   : 1.965   Min.   : 7.38   Min.   : 1.735   Min.   : 2.335  
##  1st Qu.: 4.840   1st Qu.:21.20   1st Qu.: 3.933   1st Qu.: 4.705  
##  Median :13.127   Median :34.24   Median : 5.345   Median : 6.305  
##  Mean   :14.438   Mean   :35.20   Mean   : 7.220   Mean   : 7.208  
##  3rd Qu.:18.525   3rd Qu.:48.68   3rd Qu.: 9.957   3rd Qu.: 7.975  
##  Max.   :42.422   Max.   :71.70   Max.   :23.517   Max.   :20.383  
##                   NA's   :128                      NA's   :128     
##  magesium_wood    magesium_bark   Manganese_wood   Manganese_bark   
##  Min.   :0.3400   Min.   :0.500   Min.   :  8.70   Min.   :  11.30  
##  1st Qu.:0.6425   1st Qu.:1.175   1st Qu.: 12.18   1st Qu.:  25.25  
##  Median :1.0400   Median :2.100   Median : 16.82   Median :  76.70  
##  Mean   :1.1245   Mean   :1.882   Mean   : 85.39   Mean   : 202.51  
##  3rd Qu.:1.5050   3rd Qu.:2.505   3rd Qu.: 66.08   3rd Qu.: 254.20  
##  Max.   :2.2675   Max.   :3.417   Max.   :586.92   Max.   :1265.15  
##                   NA's   :128                      NA's   :128      
##  Phosphorous_wood Phosphorous_bark  sugars_wood      sugars_bark   
##  Min.   :0.2000   Min.   :0.390    Min.   :0.1600   Min.   :0.110  
##  1st Qu.:0.6150   1st Qu.:0.605    1st Qu.:0.3937   1st Qu.:0.420  
##  Median :0.8075   Median :0.965    Median :0.9200   Median :0.990  
##  Mean   :1.2183   Mean   :1.191    Mean   :1.0647   Mean   :1.380  
##  3rd Qu.:1.3467   3rd Qu.:1.590    3rd Qu.:1.4975   3rd Qu.:1.900  
##  Max.   :4.0950   Max.   :3.840    Max.   :4.5300   Max.   :6.195  
##                   NA's   :128                       NA's   :128    
##   Silicon_wood      Silicon_bark     mean_density    density_class      block  
##  Min.   : 0.1750   Min.   : 0.340   Min.   :0.4083   Length:1531        1:385  
##  1st Qu.: 0.5000   1st Qu.: 0.770   1st Qu.:0.4657   Class :character   2:383  
##  Median : 0.5975   Median : 1.580   Median :0.5008   Mode  :character   3:382  
##  Mean   : 1.6684   Mean   : 4.436   Mean   :0.5269                      4:381  
##  3rd Qu.: 1.3725   3rd Qu.: 2.685   3rd Qu.:0.5580                             
##  Max.   :12.9000   Max.   :31.580   Max.   :0.8475                             
##                    NA's   :128                                                 
##  Harvest    Time_months  incubation_time log_fresh_weight  wood_weight     
##  1:384            :  0   6 :384          Min.   :  0.00   Min.   :  0.010  
##  2:384   12 months:384   12:384          1st Qu.: 17.87   1st Qu.:  0.010  
##  3:385   18 months:385   18:385          Median : 45.30   Median :  0.010  
##  4:378   24 months:378   24:378          Mean   : 59.10   Mean   :  8.901  
##          6 months :384                   3rd Qu.: 83.49   3rd Qu.:  0.010  
##                                          Max.   :386.32   Max.   :182.400  
##                                          NA's   :5                         
##  disk_fresh_weight disk_fresh_volume disk_dry_weight  disk_dry_volume   
##  Min.   : 0.100    Min.   :  0.000   Min.   : 0.100   Min.   :   0.000  
##  1st Qu.: 1.881    1st Qu.:  4.206   1st Qu.: 1.218   1st Qu.:   3.520  
##  Median : 4.410    Median :  8.523   Median : 2.745   Median :   7.425  
##  Mean   : 5.793    Mean   : 10.767   Mean   : 3.621   Mean   :  10.538  
##  3rd Qu.: 7.875    3rd Qu.: 14.773   3rd Qu.: 4.945   3rd Qu.:  13.010  
##  Max.   :47.450    Max.   :238.445   Max.   :28.005   Max.   :1972.775  
##  NA's   :5         NA's   :5         NA's   :5        NA's   :6         
##  Bark_fresh_mass_at_harvest Bark_fresh_volume_at_harvest
##  Min.   : 0.100             Min.   : 0.0000             
##  1st Qu.: 0.100             1st Qu.: 0.0000             
##  Median : 0.100             Median : 0.0000             
##  Mean   : 1.099             Mean   : 0.7113             
##  3rd Qu.: 0.100             3rd Qu.: 0.0000             
##  Max.   :38.000             Max.   :34.2700             
##                                                         
##  Dry_bark_mass_at_harvest Dry_bark_volume_at_harvest bark_mass_fresh 
##  Min.   : 0.1000          Min.   : 0.0000            Min.   :-36.67  
##  1st Qu.: 0.1000          1st Qu.: 0.0000            1st Qu.: 10.92  
##  Median : 0.1000          Median : 0.0000            Median : 36.24  
##  Mean   : 0.6319          Mean   : 0.5135            Mean   : 50.28  
##  3rd Qu.: 0.1000          3rd Qu.: 0.0000            3rd Qu.: 70.51  
##  Max.   :15.5450          Max.   :30.3150            Max.   :386.42  
##                                                      NA's   :5       
##   f_wood_mass         f_bark_mass        final_mass         per_ml      
##  Min.   :1.335e-03   Min.   :-20.200   Min.   :  0.00   Min.   :-52.24  
##  1st Qu.:7.362e-03   1st Qu.:  8.466   1st Qu.: 11.37   1st Qu.: 26.64  
##  Median :8.342e-03   Median : 32.355   Median : 28.64   Median : 49.40  
##  Mean   :4.032e+00   Mean   : 48.522   Mean   : 37.13   Mean   : 51.10  
##  3rd Qu.:1.000e-02   3rd Qu.: 67.785   3rd Qu.: 52.45   3rd Qu.: 78.26  
##  Max.   :1.009e+02   Max.   :386.420   Max.   :235.69   Max.   :100.00  
##  NA's   :5           NA's   :5         NA's   :5        NA's   :19
dim(hav_datb)
## [1] 1531   73
summary(hav_datb$species)
##      Ayenia grandifolia       Bauhinia purpurea     Bridelia stipularis 
##                      64                      64                      64 
##       Calamus henryanus       Cayratia trifolia           Celastrus sp1 
##                      64                      62                      63 
##           Celastrus sp2   Cheniella touranensis Cunninghamia lanceolata 
##                      64                      64                      63 
##   Eucalyptus citriodora         Ficus altissima         Iodes vitiginea 
##                      65                      64                      64 
##      Kleinhovia hospita   Macaranga denticulata            Mesua ferrea 
##                      64                      64                      64 
##       Piper flaviflorum        Piper umbellatum   Senegalia pruinescens 
##                      63                      64                      64 
##         Shorea assamica         Tectona grandis Thyrsostachys siamensis 
##                      63                      64                      64 
##           Toona ciliata           Urceola rosea     Ventilago leiocarpa 
##                      64                      64                      64
spc_dens2<- hav_datb%>% group_by( Harvest,block, species,growth_form,mesh_size ,diameter_class, Tag)%>% 
  summarize(mean(per_ml))
dim(spc_dens2)
## [1] 1531    8
dim(hav_datb)
## [1] 1531   73
havest_data<- subset(hav_datb, per_ml >0)
dim(havest_data)
## [1] 1427   73
library(tidyverse)
hav_datb%>% group_by(growth_form,Harvest,block)%>% count(species)
## # A tibble: 384 × 5
## # Groups:   growth_form, Harvest, block [32]
##    growth_form Harvest block species                   n
##    <fct>       <fct>   <fct> <fct>                 <int>
##  1 Lianas      1       1     Ayenia grandifolia        4
##  2 Lianas      1       1     Bridelia stipularis       4
##  3 Lianas      1       1     Calamus henryanus         4
##  4 Lianas      1       1     Cayratia trifolia         4
##  5 Lianas      1       1     Celastrus sp1             4
##  6 Lianas      1       1     Celastrus sp2             4
##  7 Lianas      1       1     Cheniella touranensis     4
##  8 Lianas      1       1     Iodes vitiginea           4
##  9 Lianas      1       1     Piper flaviflorum         4
## 10 Lianas      1       1     Senegalia pruinescens     4
## # ℹ 374 more rows

8 Import the phylogeny tree

Here we need to plot the phylogeny tree of the species we use in our experiment.

library(ggtree)
library(phytools)
library(phylobase)
trb<-read.tree(file="Figure.1b.tre")
plot(trb)

#ggtree(trb)+ geom_tiplab()

species_order<- c("Bridelia stipularis","Mesua ferrea","Macaranga denticulata","Celastrus sp1","Celastrus sp2","Bauhinia purpurea","Senegalia pruinescens",
                "Cheniella touranensis", "Ventilago leiocarpa", "Ficus altissima","Kleinhovia hospita","Ayenia grandifolia","Shorea assamica","Toona ciliata",
                "Eucalyptus citriodora","Cayratia trifolia","Urceola rosea","Tectona grandis","Iodes vitiginea","Calamus henryanus","Thyrsostachys siamensis",
                "Piper umbellatum","Piper flaviflorum","Cunninghamia lanceolata")

9 Average mass loss in every collection month across species, mesh size and diameter class

massloss<- hav_datb %>% group_by(species, diameter_class,mesh_size, incubation_time,growth_form)%>% 
  summarize(meanmass=mean(per_ml,na.rm = TRUE),
              se=sd(per_ml,na.rm = TRUE)/sqrt(n()),
            
  )
massloss$species<-factor(massloss$species,levels = species_order)
massloss$species<- fct_rev(massloss$species)

summary(massloss)
##                     species    diameter_class                 mesh_size  
##  Cunninghamia lanceolata: 16   2.5 cm:192     Invertebrates access :192  
##  Piper flaviflorum      : 16   5.0 cm:192     Invertebrates blocked:192  
##  Piper umbellatum       : 16                                             
##  Thyrsostachys siamensis: 16                                             
##  Calamus henryanus      : 16                                             
##  Iodes vitiginea        : 16                                             
##  (Other)                :288                                             
##  incubation_time growth_form     meanmass            se        
##  6 :96           Lianas:192   Min.   :-15.07   Min.   : 0.000  
##  12:96           Trees :192   1st Qu.: 30.13   1st Qu.: 5.257  
##  18:96                        Median : 50.32   Median : 9.195  
##  24:96                        Mean   : 50.81   Mean   : 9.717  
##                               3rd Qu.: 71.25   3rd Qu.:13.045  
##                               Max.   :100.00   Max.   :28.421  
##                                                NA's   :1
coarse_k<- massloss[massloss$mesh_size %in% c("Invertebrates access"), ]
fine_k<- massloss[massloss$mesh_size %in% c("Invertebrates blocked"), ]

fine_small<- fine_k[fine_k$diameter_class %in% c("2.5 cm"), ]
fine_small_6<- fine_small[fine_small$incubation_time %in% c("6"), ]

plot(trb)

meta_data <-  data.frame(ID = fine_small_6$species, 
                         group = fine_small_6$growth_form)
xm<- meta_data %>%
  mutate(ID = str_replace(ID, " ", "_"))

x <- full_join(as_tibble(trb), xm, by = c("label" = "ID"))
x
## # A tbl_tree abstraction: 47 × 5
## # which can be converted to treedata or phylo 
## # via as.treedata or as.phylo
##    parent  node branch.length label                 group 
##     <int> <int>         <dbl> <chr>                 <fct> 
##  1     30     1          89.8 Tectona_grandis       Trees 
##  2     30     2          89.8 Urceola_rosea         Lianas
##  3     29     3         102.  Iodes_vitiginea       Lianas
##  4     35     4          84.8 Cheniella_touranensis Lianas
##  5     36     5          84.0 Senegalia_pruinescens Lianas
##  6     36     6          84.0 Bauhinia_purpurea     Trees 
##  7     37     7          85.5 Ficus_altissima       Trees 
##  8     37     8          85.5 Ventilago_leiocarpa   Lianas
##  9     40     9          94.9 Mesua_ferrea          Trees 
## 10     40    10          94.9 Bridelia_stipularis   Lianas
## # ℹ 37 more rows
library(treeio)
tree2 <- as.treedata(x)

xt<- ggtree(tree2) + geom_tiplab(aes(color = group,fontface="bold.italic",vjust=0))+xlim(0,420)+
  theme(legend.position = c(0.2,0.9)) + labs(colour= "Growth form")+
  theme(legend.direction = "vertical", legend.box = "vertical",
        legend.text=element_text(size=12,face="bold"),
        legend.background = element_blank(),
        legend.key.width=unit(1,"cm"),
        legend.key.height=unit(1,"cm"),
        legend.title = element_text(size=12, face= "bold"))+
geom_text (aes (280, 24.3), label = 'Phyllanthaceae', check_overlap = TRUE, color = 'black', size = 3)+
  geom_text (aes (280, 23.3), label = 'Calophyllaceae', check_overlap = TRUE, color = 'black', size = 3)+
  geom_text (aes (280, 22.3), label = 'Euphorbiaceae', check_overlap = TRUE, color = 'black', size = 3)+
  geom_text (aes (280, 20.9), label = 'Celastraceae', check_overlap = TRUE, color = 'black', size = 3)+
  geom_text (aes (290, 19.3), label = 'Fabaceae', check_overlap = TRUE, color = 'black', size = 3)+
  geom_text (aes (280, 16.3), label = 'Rhamnaceae', check_overlap = TRUE, color = 'black', size = 3)+
  geom_text (aes (280, 15.3), label = 'Moraceae', check_overlap = TRUE, color = 'black', size = 3)+
  geom_text (aes (280, 13.9), label = 'Malvaceae', check_overlap = TRUE, color = 'black', size = 3)+
  geom_text (aes (290, 12.3), label = 'Dipterocarpaceae', check_overlap = TRUE, color = 'black', size = 3)+
  geom_text (aes (300, 11.3), label = 'Meliaceae', check_overlap = TRUE, color = 'black', size = 3)+
  geom_text (aes (300, 10.3), label = 'Myrtaceae', check_overlap = TRUE, color = 'black', size = 3)+
  geom_text (aes (300, 9.3), label = 'Vitaceae', check_overlap = TRUE, color = 'black', size = 3)+
  geom_text (aes (280, 8.3), label = 'Apocynaceae', check_overlap = TRUE, color = 'black', size = 3)+
  geom_text (aes (280, 7.3), label = 'Lamiaceae', check_overlap = TRUE, color = 'black', size = 3)+
  geom_text (aes (280, 6.3), label = 'Icacinaceae', check_overlap = TRUE, color = 'black', size = 3)+
  geom_text (aes (260, 5.3), label = 'Arecaceae', check_overlap = TRUE, color = 'black', size = 3)+
  geom_text (aes (260, 4.3), label = 'Poaceae', check_overlap = TRUE, color = 'black', size = 3)+
  geom_text (aes (260, 2.8), label = 'Piperaceae', check_overlap = TRUE, color = 'black', size = 3)+
  geom_text (aes (260, 1.3), label = 'Cupressaceae', check_overlap = TRUE, color = 'black', size = 3)

xt

Figure S1 Phylogenetic tree showing the relationship between liana and tree species selected for this experiment

### Phylogenetic tree showing the relationship between liana and tree species selected for this experiment.
ggsave(filename="Figure S1.png", plot=xt, device="png",
       path=path, height=6, width=8, units="in", dpi=500)
fine_small<- fine_k[fine_k$diameter_class %in% c("2.5 cm"), ]
fine_small_6<- fine_small[fine_small$incubation_time %in% c("6"), ]

sm_fn6<- ggplot(fine_small_6, aes(y = species, x= meanmass,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar(xmin= fine_small_6$meanmass-fine_small_6$se, xmax=fine_small_6$meanmass+fine_small_6$se, width=0.5)+ scale_x_continuous(limits=c(-30,60))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_text(size=9, face= "italic") ,
        axis.text.x =element_text(size=10,colour="black"),
        axis.title.x =element_text(size=12, colour = "black"))+ 
  labs(x= "Percentage mass loss",fill= "Growth form")+ theme(legend.position = c(0.2,0.7),legend.background = element_blank())+
ggtitle(label="2.5 cm wood- inverterbrates absent", subtitle ="6 months")+ theme(
    plot.title = element_text(color = "black", size = 12, face = "bold"),
    plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#sm_fn6

fine_small<- fine_k[fine_k$diameter_class %in% c("2.5 cm"), ]
fine_small_12<- fine_small[fine_small$incubation_time %in% c("12"), ]

sm_fn12<- ggplot(fine_small_12, aes(y = species, x= meanmass,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar(xmin= fine_small_12$meanmass-fine_small_12$se, xmax=fine_small_12$meanmass+fine_small_12$se, width=0.5)+  
  scale_x_continuous(limits=c(-15,60))+
  guides(fill = guide_colourbar(title = "Growth form"))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_blank() ,
        axis.text.x =element_text(size=9,colour="black"),
        axis.title.x =element_text(size=12, colour = "black"))+ 
  labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
  ggtitle(label="",subtitle ="12 months")+ theme(
    plot.title = element_text(color = "black", size = 12, face = "bold"),
    plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#sm_fn12

fine_small<- fine_k[fine_k$diameter_class %in% c("2.5 cm"), ]
fine_small_18<- fine_small[fine_small$incubation_time %in% c("18"), ]

sm_fn18<- ggplot(fine_small_18, aes(y = species, x= meanmass,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar(xmin= fine_small_18$meanmass-fine_small_18$se, xmax=fine_small_18$meanmass+fine_small_18$se, width=0.5)+  
  scale_x_continuous(limits=c(-5,90))+
  guides(fill = guide_colourbar(title = "Growth form"))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_blank() ,
        axis.text.x =element_text(size=9,colour="black"),
        axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
ggtitle(label="", subtitle ="18 months")+ theme(
    plot.title = element_text(color = "black", size = 12, face = "bold"),
    plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#sm_fn18


fine_small<- fine_k[fine_k$diameter_class %in% c("2.5 cm"), ]
fine_small_24<- fine_small[fine_small$incubation_time %in% c("24"), ]

sm_fn24<- ggplot(fine_small_24, aes(y = species, x= meanmass,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar(xmin= fine_small_24$meanmass-fine_small_24$se, xmax=fine_small_24$meanmass+fine_small_24$se, width=0.5)+  
  scale_x_continuous(limits=c(0,90))+
  guides(fill = guide_colourbar(title = "Growth form"))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_blank() ,
        axis.text.x =element_text(size=9,colour="black"),
        axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
 ggtitle(label="", subtitle ="24 months")+ theme(
    plot.title = element_text(color = "black", size = 12, face = "bold"),
    plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#sm_fn24
fine_big<- fine_k[fine_k$diameter_class %in% c("5.0 cm"), ]
fine_big_6<- fine_big[fine_big$incubation_time %in% c("6"), ]

bg_fn6<- ggplot(fine_big_6, aes(y = species, x= meanmass,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar(xmin= fine_big_6$meanmass-fine_big_6$se, xmax=fine_big_6$meanmass+fine_big_6$se, width=0.5)+  scale_x_continuous(limits=c(-30,60))+
  guides(fill = guide_colourbar(title = "Growth form"))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_text(size=9, face= "italic") ,
        axis.text.x =element_text(size=10,colour="black"),
        axis.title.x =element_text(size=12, colour = "black"))+ 
  labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
  ggtitle(label= "
4.0 cm - Inverterbrates absent", subtitle ="6 months")+ theme(
    plot.title = element_text(color = "black", size = 12, face = "bold"),
    plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#bg_fn6

fine_big<- fine_k[fine_k$diameter_class %in% c("5.0 cm"), ]
fine_big_12<- fine_big[fine_big$incubation_time %in% c("12"), ]

bg_fn12<- ggplot(fine_big_12, aes(y = species, x= meanmass,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar(xmin= fine_big_12$meanmass-fine_big_12$se, xmax=fine_big_12$meanmass+fine_big_12$se, width=0.5)+  scale_x_continuous(limits=c(-15,60))+
  guides(fill = guide_colourbar(title = "Growth form"))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_blank() ,
        axis.text.x =element_text(size=10,colour="black"),
        axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
  ggtitle(label="", subtitle ="12 months")+ theme(
    plot.title = element_text(color = "black", size = 12, face = "bold"),
    plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))

#bg_fn12

fine_big<- fine_k[fine_k$diameter_class %in% c("5.0 cm"), ]
fine_big_18<- fine_big[fine_big$incubation_time %in% c("18"), ]

bg_fn18<- ggplot(fine_big_18, aes(y = species, x= meanmass,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar(xmin= fine_big_18$meanmass-fine_big_18$se, xmax=fine_big_18$meanmass+fine_big_18$se, width=0.5)+  scale_x_continuous(limits=c(-5,90))+
  guides(fill = guide_colourbar(title = "Growth form"))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_blank() ,
        axis.text.x =element_text(size=9,,colour="black"),
        axis.title.x =element_text(size=9, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
  ggtitle(label="",subtitle ="18 months")+ theme(
    plot.title = element_text(color = "black", size = 12, face = "bold"),
    plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#bg_fn18


fine_big<- fine_k[fine_k$diameter_class %in% c("5.0 cm"), ]
fine_big_24<- fine_big[fine_big$incubation_time %in% c("24"), ]

bg_fn24<- ggplot(fine_big_24, aes(y = species, x= meanmass,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar(xmin= fine_big_24$meanmass-fine_big_24$se, xmax=fine_big_24$meanmass+fine_big_24$se, width=0.5)+  scale_x_continuous(limits=c(0,90))+
  guides(fill = guide_colourbar(title = "Growth form"))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_blank() ,
        axis.text.x =element_text(size=9,colour="black"),
        axis.title.x =element_text(size=9, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
  ggtitle(label="",subtitle ="24 months")+ theme(
    plot.title = element_text(color = "black", size = 12, face = "bold"),
    plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#bg_fn24
#FigS1<- sm_fn6+ sm_fn12+sm_fn18+sm_fn24+bg_fn6+bg_fn12+bg_fn18+bg_fn24+ plot_layout(ncol = 4)
#FigS1
#ggsave(filename="FigS1.png", plot=FigS1, device="png",
#       path=path, height=9, width=16, units="in", dpi=500)
coarse_small<- coarse_k[coarse_k$diameter_class %in% c("2.5 cm"), ]
coarse_small_6<- coarse_small[coarse_small$incubation_time %in% c("6"), ]

sm_crs6<- ggplot(coarse_small_6, aes(y = species, x= meanmass,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar(xmin= coarse_small_6$meanmass-coarse_small_6$se,xmax=coarse_small_6$meanmass+coarse_small_6$se, width=0.5)+  
  guides(fill = guide_colourbar(title = "Growth form"))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_text(size=9,face="italic") ,
        axis.text.x =element_text(size=10,colour="black"),
        axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
  ggtitle(label="2.5 cm wood - inverterbrates present", subtitle ="6 months")+ theme(
    plot.title = element_text(color = "black", size = 12, face = "bold"),
    plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#sm_crs6

coarse_small<- coarse_k[coarse_k$diameter_class %in% c("2.5 cm"), ]
coarse_small_12<- coarse_small[coarse_small$incubation_time %in% c("12"), ]

sm_crs12<- ggplot(coarse_small_12, aes(y = species, x= meanmass,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar(xmin= coarse_small_12$meanmass-coarse_small_12$se,xmax=coarse_small_12$meanmass+coarse_small_12$se, width=0.5)+  
  guides(fill = guide_colourbar(title = "Growth form"))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_blank() ,
        axis.text.x =element_text(size=10,colour="black"),
        axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
  ggtitle(label="", subtitle ="12 months")+ theme(
    plot.title = element_text(color = "black", size = 12, face = "bold"),
    plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#sm_crs12


coarse_small_18<- coarse_small[coarse_small$incubation_time %in% c("18"), ]

sm_crs18<- ggplot(coarse_small_18, aes(y = species, x= meanmass,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar(xmin= coarse_small_18$meanmass-coarse_small_18$se,xmax=coarse_small_18$meanmass+coarse_small_18$se, width=0.5)+  
  guides(fill = guide_colourbar(title = "Growth form"))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_blank() ,
        axis.text.x =element_text(size=10,colour="black"),
        axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
  ggtitle(label="", subtitle ="18 months")+ theme(
    plot.title = element_text(color = "black", size = 12, face = "bold"),
    plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#sm_crs18


coarse_small_24<- coarse_small[coarse_small$incubation_time %in% c("24"), ]

sm_crs24<- ggplot(coarse_small_24, aes(y = species, x= meanmass,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar(xmin= coarse_small_24$meanmass-coarse_small_24$se,xmax=coarse_small_24$meanmass+coarse_small_24$se, width=0.5)+  
  guides(fill = guide_colourbar(title = "Growth form"))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_blank() ,
        axis.text.x =element_text(size=10,colour="black"),
        axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
  ggtitle(label="", subtitle ="24 months")+ theme(
    plot.title = element_text(color = "black", size = 12, face = "bold"),
    plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#sm_crs24
coarse_big<- coarse_k[coarse_k$diameter_class %in% c("5.0 cm"), ]
coarse_big_6<- coarse_big[coarse_big$incubation_time %in% c("6"), ]

bg_crs6<- ggplot(coarse_big_6, aes(y = species, x= meanmass,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar(xmin= coarse_big_6$meanmass-coarse_big_6$se,xmax=coarse_big_6$meanmass+coarse_big_6$se, width=0.5)+  
  guides(fill = guide_colourbar(title = "Growth form"))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_text(size = 9, face = "italic") ,
        axis.text.x =element_text(size=10,colour="black"),
        axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
  ggtitle(label="4.0 cm wood - inverterbrates present", subtitle =" 6 months")+ theme(
    plot.title = element_text(color = "black", size = 12, face = "bold"),
    plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#bg_crs6

coarse_big<- coarse_k[coarse_k$diameter_class %in% c("5.0 cm"), ]
coarse_big_12<- coarse_big[coarse_big$incubation_time %in% c("12"), ]

bg_crs12<- ggplot(coarse_big_12, aes(y = species, x= meanmass,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar(xmin= coarse_big_12$meanmass-coarse_big_12$se,xmax=coarse_big_12$meanmass+coarse_big_12$se, width=0.5)+  
  guides(fill = guide_colourbar(title = "Growth form"))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_blank() ,
        axis.text.x =element_text(size=10,colour="black"),
        axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
  ggtitle(label="", subtitle ="12 months")+ theme(
    plot.title = element_text(color = "black", size = 12, face = "bold"),
    plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#bg_crs12


coarse_big_18<- coarse_big[coarse_big$incubation_time %in% c("18"), ]

bg_crs18<- ggplot(coarse_big_18, aes(y = species, x= meanmass,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar(xmin= coarse_big_18$meanmass-coarse_big_18$se,xmax=coarse_big_18$meanmass+coarse_big_18$se, width=0.5)+  
  guides(fill = guide_colourbar(title = "Growth form"))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_blank() ,
        axis.text.x =element_text(size=10,colour="black"),
        axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
  ggtitle(label="", subtitle ="18 months")+ theme(
    plot.title = element_text(color = "black", size = 12, face = "bold"),
    plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#bg_crs18


coarse_big_24<- coarse_big[coarse_big$incubation_time %in% c("24"), ]

bg_crs24<- ggplot(coarse_big_24, aes(y = species, x= meanmass,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar(xmin= coarse_big_24$meanmass-coarse_big_24$se,xmax=coarse_big_24$meanmass+coarse_big_24$se, width=0.5)+  
  guides(fill = guide_colourbar(title = "Growth form"))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_blank() ,
        axis.text.x =element_text(size=10,colour="black"),
        axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
  ggtitle(label="", subtitle ="24 months")+ theme(
    plot.title = element_text(color = "black", size = 12, face = "bold"),
    plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#bg_crs24
#library(patchwork)
#FigS2<- sm_crs6+sm_crs12+sm_crs18+sm_crs24+bg_crs6+bg_crs12+bg_crs18+bg_crs24+ plot_layout(ncol = 4)
#FigS2
#ggsave(filename="FigS2.png", plot=FigS2, device="png",
#       path=path, height=9, width=16, units="in", dpi=500)
forth_harvest<- hav_datb[hav_datb$Harvest %in% c("4"), ]

forth_harvest$W_D_prop<- forth_harvest$f_bark_mass-0.01/forth_harvest$f_wood_mass-0.01

#write.csv(forth_harvest, "hv4.csv")
#plot(W_D_prop~ growth_form, data= forth_harvest)

#b_w_prop_fig<- ggplot(data = forth_harvest, aes(x = growth_form, y =W_D_prop)) + theme_bw()+
#  theme(legend.position= "none")+ 
#  labs(x= "Growth form",y="Bark to wood mass proportion")+
#  geom_boxplot(aes(fill = growth_form), color = "black")+facet_grid(rows = vars(diameter_class),cols = #vars(mesh_size),axes = "all", axis.labels = "all_x")
#b_w_prop_fig
#ggsave(filename="b_w_prop_fig.png", plot=b_w_prop_fig, device="png",
#       path=path, height=4, width=5, units="in", dpi=500)
hist(forth_harvest$W_D_prop)

md1<- lmer(W_D_prop~(growth_form+mesh_size+diameter_class)^2 + (1|species), data = forth_harvest)
anova(md1)
## Type III Analysis of Variance Table with Satterthwaite's method
##                             Sum Sq Mean Sq NumDF  DenDF  F value    Pr(>F)    
## growth_form                  479.8   479.8     1  22.00   6.9695  0.014956 *  
## mesh_size                  12086.7 12086.7     1 349.10 175.5735 < 2.2e-16 ***
## diameter_class              2725.1  2725.1     1 350.23  39.5849 9.377e-10 ***
## growth_form:mesh_size        712.2   712.2     1 349.10  10.3459  0.001419 ** 
## growth_form:diameter_class   268.3   268.3     1 350.23   3.8975  0.049143 *  
## mesh_size:diameter_class    1630.8  1630.8     1 349.34  23.6886 1.718e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
forth_harvest_small<- forth_harvest[forth_harvest$diameter_class == "2.5 cm",]
forth_harvest_big<- forth_harvest[forth_harvest$diameter_class == "5.0 cm",]

forth_harvest_small_fine<- forth_harvest_small[forth_harvest_small$mesh_size == "Invertebrates blocked",]
forth_harvest_small_crs<- forth_harvest_small[forth_harvest_small$mesh_size == "Invertebrates access",]

forth_harvest_big_fine<- forth_harvest_big[forth_harvest_big$mesh_size == "Invertebrates blocked",]
forth_harvest_big_crs<- forth_harvest_big[forth_harvest_big$mesh_size == "Invertebrates access",]


hist(forth_harvest_small_fine$W_D_prop)

hist(log(forth_harvest_small_fine$W_D_prop))

md_sm_fn<- lmer(W_D_prop~ growth_form + (1|species), data = forth_harvest_small_fine)
anova(md_sm_fn)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)  
## growth_form 152.19  152.19     1 22.256  4.9288 0.0369 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(md_sm_fn)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: W_D_prop ~ growth_form + (1 | species)
##    Data: forth_harvest_small_fine
## 
## REML criterion at convergence: 634.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.1015 -0.4769 -0.1278  0.3997  3.4265 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 24.49    4.949   
##  Residual             30.88    5.557   
## Number of obs: 97, groups:  species, 24
## 
## Fixed effects:
##                  Estimate Std. Error     df t value Pr(>|t|)    
## (Intercept)        11.851      1.633 21.956   7.259 2.88e-07 ***
## growth_formTrees   -5.143      2.316 22.256  -2.220   0.0369 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## grwth_frmTr -0.705
r.squaredGLMM(md_sm_fn)
##            R2m      R2c
## [1,] 0.1075753 0.502334
dim(forth_harvest_small_fine)
## [1] 97 74
lblbwratio<-expression(paste( F["(1,23)"],"   =   4.92")) 

plot_sm_fn<- ggplot(data = forth_harvest_small_fine, aes(x = growth_form, y =W_D_prop)) + theme_bw()+
  theme(legend.position= "none")+
  labs(x= "Growth form",y="Bark to wood mass proportion")+ggtitle("Inverterbrates blocked (2.5 cm)")+ geom_boxplot(aes(fill = growth_form), color = "black")+ 
  annotate("text", x = 2, y = 30, size=3.5, hjust=0.5,label = as.character(lblbwratio), parse=TRUE) +
  annotate("text", x = 2, y = 25, size=3.5, label = "p = 0.03")+
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 9),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank())
plot_sm_fn

hist(forth_harvest_big_fine$W_D_prop)

md_big_fn<- lmer(W_D_prop~ growth_form + (1|species), data = forth_harvest_big_fine)
anova(md_big_fn)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)  
## growth_form 574.02  574.02     1 22.011  5.4955 0.0285 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
dim(forth_harvest_big_fine)
## [1] 94 74
plot_big_fn<-ggplot(data = forth_harvest_big_fine, aes(x = growth_form, y =W_D_prop)) + theme_bw()+
  theme(legend.position= "none")+
  labs(x= "Growth form",y="Bark to wood mass proportion")+ ggtitle("Invertebrates blocked (4.0 cm)")+ geom_boxplot(aes(fill = growth_form), color = "black")+ 
  annotate("text", x = 2, y = 50, size=3.5, hjust=0.5,label = as.character(expression(paste( F["(1,22)"],"   =   5.49")))) +
  annotate("text", x = 2, y = 45, size=3.5, label = "p = 0.03")+
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 9),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank())
plot_big_fn

hist(forth_harvest_big_crs$W_D_prop)

hist(log(forth_harvest_big_crs$W_D_prop))

hist(forth_harvest_big_crs$W_D_prop^(2))

md_big_crs<- lmer(W_D_prop~ growth_form + (1|species), data = forth_harvest_big_crs)
anova(md_big_crs)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## growth_form 49.147  49.147     1 21.594  1.9337 0.1785
dim(forth_harvest_big_fine)
## [1] 94 74
plot_big_crs<- ggplot(data = forth_harvest_big_crs, aes(x = growth_form, y =W_D_prop)) + theme_bw()+
  theme(legend.position= "none")+
  labs(x= "Growth form",y="Bark to wood mass proportion")+
  geom_boxplot(aes(fill = growth_form), color = "black")+ ggtitle("Invertebrates access (4.0 cm)")+
  annotate("text", x = 1.6, y = 20, size=5.5, hjust=0.5,label = "")+
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 9),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank())


hist(forth_harvest_small_crs$W_D_prop)

hist(log(forth_harvest_small_crs$W_D_prop)+3.5)

hist(forth_harvest_small_crs$W_D_prop^(2))

md_small_crs<- lmer(log(W_D_prop)~ growth_form + (1|species), data = forth_harvest_small_crs)
anova(md_small_crs)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
## growth_form 13.104  13.104     1 14.925  5.2656 0.03668 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lbl_small_crs<-expression(paste( F["(1,14)"],"   =   5.26"))
dim(forth_harvest_big_fine)
## [1] 94 74
plot_small_crs<- ggplot(data = forth_harvest_small_crs, aes(x = growth_form, y =W_D_prop)) + theme_bw()+
  theme(legend.position= "none")+
  labs(x= "Growth form",y="Bark to wood mass proportion")+
  ggtitle("Invertebrates access (2.5 cm)")+
  geom_boxplot(aes(fill = growth_form), color = "black")+ 
    annotate("text", x = 2, y = 30, size=3.5, hjust=0.5,label = as.character()) +
  annotate("text", x = 2, y = 25, size=3.5, label = "p = 0.03")+
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 9),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank())
plot_small_crs

hv_4th_plt<- plot_big_crs+plot_big_fn+plot_small_crs+plot_sm_fn
#hv_4th_plt

### Plot the proportion of bark to wood dry mass ratio for lianas and trees after 24 months of field incubation
ggsave(filename="Figure 2.png", plot=hv_4th_plt, device="png",
       path=path, height=6, width=7, units="in", dpi=500)

10 Plot the mass loss of species

First we calculate the mean mass loss per species. To test hypothesis one we compare the diameter_class of lianas and trees WD in the absence of inverterbrates to do so we create a new data frame for the fine mesh size litterbags (Invertebrates blocked )

10.1 calculate invertebrates contribution to WD decomposition across different growthforms and WD diameter_class

ml_species<-hav_datb%>%
  group_by(Time_months,mesh_size,diameter_class,growth_form,species)%>%
  summarize(ml=mean(per_ml,na.rm = TRUE),
            
  )
summary(ml_species)
##     Time_months                 mesh_size   diameter_class growth_form 
##           : 0   Invertebrates access :192   2.5 cm:192     Lianas:192  
##  12 months:96   Invertebrates blocked:192   5.0 cm:192     Trees :192  
##  18 months:96                                                          
##  24 months:96                                                          
##  6 months :96                                                          
##                                                                        
##                                                                        
##                 species          ml        
##  Ayenia grandifolia : 16   Min.   :-15.07  
##  Bauhinia purpurea  : 16   1st Qu.: 30.13  
##  Bridelia stipularis: 16   Median : 50.32  
##  Calamus henryanus  : 16   Mean   : 50.81  
##  Cayratia trifolia  : 16   3rd Qu.: 71.25  
##  Celastrus sp1      : 16   Max.   :100.00  
##  (Other)            :288
#spread stat column across multiple columns
ml_wide<- spread(ml_species, key=mesh_size, value=ml)
ml_wide$invt_ml<- ml_wide$`Invertebrates access` - ml_wide$`Invertebrates blocked`
summary(ml_wide)
##     Time_months diameter_class growth_form                species   
##           : 0   2.5 cm:96      Lianas:96   Ayenia grandifolia :  8  
##  12 months:48   5.0 cm:96      Trees :96   Bauhinia purpurea  :  8  
##  18 months:48                              Bridelia stipularis:  8  
##  24 months:48                              Calamus henryanus  :  8  
##  6 months :48                              Cayratia trifolia  :  8  
##                                            Celastrus sp1      :  8  
##                                            (Other)            :144  
##  Invertebrates access Invertebrates blocked    invt_ml      
##  Min.   :  3.035      Min.   :-15.07        Min.   :-12.29  
##  1st Qu.: 49.902      1st Qu.: 19.89        1st Qu.: 18.44  
##  Median : 68.657      Median : 35.15        Median : 29.87  
##  Mean   : 65.963      Mean   : 35.66        Mean   : 30.30  
##  3rd Qu.: 82.054      3rd Qu.: 51.11        3rd Qu.: 40.46  
##  Max.   :100.000      Max.   : 88.24        Max.   : 89.07  
## 
inv_spec<-ml_wide%>%
  group_by(Time_months,diameter_class, growth_form)%>%
  summarize(mean_invt=mean(invt_ml,na.rm = TRUE),
            sd=sd(invt_ml,na.rm = TRUE),
            se=sd(invt_ml,na.rm = TRUE)/sqrt(n()),
            
  )
inv_spec
## # A tibble: 16 × 6
## # Groups:   Time_months, diameter_class [8]
##    Time_months diameter_class growth_form mean_invt    sd    se
##    <fct>       <fct>          <fct>           <dbl> <dbl> <dbl>
##  1 12 months   2.5 cm         Lianas           35.2  23.3  6.73
##  2 12 months   2.5 cm         Trees            29.4  14.8  4.26
##  3 12 months   5.0 cm         Lianas           40.7  16.5  4.77
##  4 12 months   5.0 cm         Trees            30.2  15.6  4.50
##  5 18 months   2.5 cm         Lianas           25.2  17.7  5.10
##  6 18 months   2.5 cm         Trees            29.1  10.7  3.09
##  7 18 months   5.0 cm         Lianas           31.7  14.2  4.10
##  8 18 months   5.0 cm         Trees            26.8  17.3  4.99
##  9 24 months   2.5 cm         Lianas           35.9  16.0  4.61
## 10 24 months   2.5 cm         Trees            29.3  16.4  4.72
## 11 24 months   5.0 cm         Lianas           28.5  18.3  5.27
## 12 24 months   5.0 cm         Trees            29.7  12.4  3.57
## 13 6 months    2.5 cm         Lianas           26.8  28.2  8.15
## 14 6 months    2.5 cm         Trees            26.6  20.4  5.89
## 15 6 months    5.0 cm         Lianas           34.3  13.0  3.74
## 16 6 months    5.0 cm         Trees            25.5  14.2  4.09
ml_species<-hav_datb%>%
  group_by(Time_months,mesh_size,diameter_class,growth_form)%>%
  summarize(ml=mean(per_ml,na.rm = TRUE),
            
  )
summary(ml_species)
##     Time_months                 mesh_size  diameter_class growth_form
##           :0    Invertebrates access :16   2.5 cm:16      Lianas:16  
##  12 months:8    Invertebrates blocked:16   5.0 cm:16      Trees :16  
##  18 months:8                                                         
##  24 months:8                                                         
##  6 months :8                                                         
##                                                                      
##        ml       
##  Min.   :16.10  
##  1st Qu.:37.00  
##  Median :53.11  
##  Mean   :51.02  
##  3rd Qu.:63.64  
##  Max.   :89.68

11 Decay rates for each species across mesh size and diameter class

hav_datb$incubation_time<- as.numeric(as.character(hav_datb$incubation_time))
hav_datb$time_yrs<- hav_datb$incubation_time/12
df2 <- hav_datb %>%
  filter(hav_datb$per_ml >= 0)
#df2 <- hav_datb %>%


any(is.na(df2 $mass_remaining))
## [1] FALSE
df2 $mass_remaining<- (df2$final_mass)/df2$initial_mass
df2 $incubation_time<- as.numeric(df2 $incubation_time)

dt<- df2 %>% dplyr::select(species, growth_form, block,diameter_class,mesh_size, time_yrs,mass_remaining)
data_aggregated <- dt %>%
  group_by(time_yrs, species,growth_form,mesh_size,diameter_class,block) %>% # Group by the identifier and time columns
  summarize(value = mean(mass_remaining, na.rm = TRUE)) %>% # Aggregate (take the mean)
  ungroup()
any(is.na(data_aggregated $value))
## [1] FALSE
# Reshape the data for litterfitter
wood_decomp_wide <-data_aggregated %>%
  pivot_wider(names_from = time_yrs, values_from = value,names_prefix = "time_",)
wood_decomp_wide$time_0<- 1

long_data2 <- wood_decomp_wide %>%
  pivot_longer( cols = time_0.5:time_0,names_to = "time_yrs", values_to = "mass_remaining") %>%
  mutate(time_yrs = gsub("time_", "", time_yrs)) #remove "_mass" from species name
any(is.na(long_data2$mass_remaining))
## [1] TRUE
str(long_data2)
## tibble [1,920 × 7] (S3: tbl_df/tbl/data.frame)
##  $ species       : Factor w/ 24 levels "Ayenia grandifolia",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ growth_form   : Factor w/ 2 levels "Lianas","Trees": 1 1 1 1 1 1 1 1 1 1 ...
##  $ mesh_size     : Factor w/ 2 levels "Invertebrates access",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ diameter_class: Factor w/ 2 levels "2.5 cm","5.0 cm": 1 1 1 1 1 1 1 1 1 1 ...
##  $ block         : Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 2 2 2 2 2 ...
##  $ time_yrs      : chr [1:1920] "0.5" "1" "1.5" "2" ...
##  $ mass_remaining: num [1:1920] 0.405 0.464 0.638 0.357 1 ...
long_data2$time_yrs<- as.numeric(as.character(long_data2$time_yrs))

library(litterfitter)
library(dplyr)
library(tibble)
library(litterfitter)
library(dplyr)
library(tibble)

lita<- na.omit(long_data2)
dim(lita)
## [1] 1789    7
dim(long_data2)
## [1] 1920    7
options(na.action = "na.omit")

# turn a list of dataframes into a long dataframe
list_to_df <- function(mylist){
  
  # make a vector of row ids that correspond to the list names
  rowid.indx <- lapply(mylist, function(x) dim(x)[1])
  sourceVec.list <- list()
  for(i in 1:length(rowid.indx)){
    sourceName <- names(rowid.indx)[i]
    numRows <- rowid.indx[[i]]
    sourceVec.list[[i]] <- rep(sourceName, numRows)
  }
  rowVec <- unlist(sourceVec.list)
  
  # combine into df
  df <- data.frame(do.call(rbind, mylist))
  df$source <- rowVec
  
  return(df)
}


# calculate decay trajectory fits for each species+size
Calc_R2<-function(w.fits){
  
  pred<-w.fits[['predicted']]
  mass<-w.fits[['mass']]
  
  sstot<-sum((mass - mean(mass))^2)
  ssres<-sum((pred - mass)^2)
  r2<-1-(ssres/sstot)
  
  return(r2)
}





# decay fits
fit_all_curves<-function(df_in, lita){
  
  #df_in <- pmr  #testing stuff
  
  #negative expon fit
  ne_fits <- lapply(split(df_in, factor(df_in$species)),function(x){
    fit_litter(time = x$time_yrs, 
               mass.remaining = x$mass_remaining, model = c("neg.exp"), iters = 1000)
  })
  k<-unlist(lapply(ne_fits, function(x) x$optimFit$par))
  # put everything in a dataframe
  spdf<-data.frame(k=k )
  spdf$species<-rownames(spdf)
  
  return(spdf)
} 
neg.exp<-litterfitter:::neg.exp


decayfits<-lita %>%
  group_by(species,growth_form,mesh_size,diameter_class,block) %>%
  do(data.frame(fit_all_curves(.)))
lt<-decayfits%>%
  group_by(diameter_class, growth_form,mesh_size)%>%
  summarize(mean_invt=mean(k,na.rm = TRUE),
            sd=sd(k,na.rm = TRUE),
            se=sd(k,na.rm = TRUE)/sqrt(n()),
            
  )
lt
## # A tibble: 8 × 6
## # Groups:   diameter_class, growth_form [4]
##   diameter_class growth_form mesh_size             mean_invt    sd     se
##   <fct>          <fct>       <fct>                     <dbl> <dbl>  <dbl>
## 1 2.5 cm         Lianas      Invertebrates access      1.38  0.882 0.127 
## 2 2.5 cm         Lianas      Invertebrates blocked     0.463 0.210 0.0303
## 3 2.5 cm         Trees       Invertebrates access      1.40  1.17  0.168 
## 4 2.5 cm         Trees       Invertebrates blocked     0.444 0.218 0.0314
## 5 5.0 cm         Lianas      Invertebrates access      1.59  1.27  0.184 
## 6 5.0 cm         Lianas      Invertebrates blocked     0.438 0.180 0.0261
## 7 5.0 cm         Trees       Invertebrates access      1.19  0.924 0.133 
## 8 5.0 cm         Trees       Invertebrates blocked     0.414 0.192 0.0277
dim(lt)
## [1] 8 6
lt_plot<- ggplot(lt, aes(x = diameter_class, y = as.numeric(as.character(mean_invt )),shape =mesh_size, colour=growth_form)) + ylab(NULL) +
  geom_point(pch=22,size=0, color =("white"))+geom_rect(data=NULL,aes(xmin=1.5,xmax=2.5,ymin=-Inf,ymax=Inf),
                                                        fill="lightgray", colour = NA, alpha = 0.1) +
  geom_point(position=position_dodge(0.3),stat="identity", size=2.5)+scale_y_continuous(limits = c(0,2))+
  geom_errorbar(ymin = lt$mean_invt - lt$se, ymax = lt$mean_invt +  lt$se, width=0.2, size = 0.5, position=position_dodge(0.3))+
  xlab("Diameter class") +
  ylab("Decomposition rate") + scale_x_discrete(labels= c("2.5 cm", "4.0 cm"))+
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 15),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank(),
        strip.text = element_text(colour = "black", size = 12),
        legend.position = c(0.4,0.9),
        legend.direction = "vertical", legend.box = "horizontal",
        legend.text=element_text(size=10,face="italic"),
        legend.background = element_blank(),
        legend.key.width=unit(0.1,"cm"),
        legend.key.height=unit(0.1,"cm"))+
  labs(color="Growth form",shape="Invertebrate status", size=10)+
  theme(text=element_text(size=10,  family="serif",face="bold"))+ theme(axis.title = element_text(family = "palatino", size = (12), colour = "black"))+
  theme(axis.text = element_text(family = "palatino", colour = "black", size = (8)))

Let save figure 4.

ggsave(filename="Figure 4.png", plot=lt_plot, device="png",
       path=path, height=3, width=5, units="in", dpi=500)
lt_fauna<- lt[lt$mesh_size== "Invertebrates access",]
dim(lt)
## [1] 8 6
lt_fauna_plot<- ggplot(lt_fauna, aes(x = diameter_class, y = as.numeric(as.character(mean_invt )),colour=growth_form)) + ylab(NULL) +
  geom_point(pch=22,size=0, color =("white"))+geom_rect(data=NULL,aes(xmin=1.5,xmax=2.5,ymin=-Inf,ymax=Inf),
                                                        fill="lightgray", colour = NA, alpha = 0.1) +
  geom_point(position=position_dodge(0.3),stat="identity", size=3)+scale_y_continuous(limits = c(1,2))+
  geom_errorbar(ymin = lt_fauna$mean_invt - lt_fauna$se, ymax = lt_fauna$mean_invt +  lt_fauna$se, width=0.1, size = 0.8, position=position_dodge(0.3))+
  xlab("Diameter class") +
  ylab("Decomposition rate") + scale_x_discrete(labels= c("2.5 cm", "4.0 cm"))+
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 15),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank(),
        strip.text = element_text(colour = "black", size = 12),
        legend.position = c(0.2,0.9),
        legend.direction = "vertical", legend.box = "horizontal",
        legend.text=element_text(size=10,face="italic"),
        legend.background = element_blank(),
        legend.key.width=unit(0.1,"cm"),
        legend.key.height=unit(0.1,"cm"))+
  labs(color="Growth form",shape="Invertebrate status", size=10)+
  theme(text=element_text(size=10,  family="serif",face="bold"))+ theme(axis.title = element_text(family = "palatino", size = (12), colour = "black"))+
  theme(axis.text = element_text(family = "palatino", colour = "black", size = (8)))
lt_fauna_plot

ggsave(filename="Figure 1.png", plot=lt_fauna_plot, device="png",
       path=path, height=3, width=5, units="in", dpi=500)

11.1 Merge k values with the initial traits

dt_f<- decayfits %>% left_join(traits_wide, by= c("species"))
f_d<- dt_f%>% left_join(data_density, by= c("growth_form", "species"))
dt_fb<- f_d %>% left_join(spc_dens_wdbark, by= c("species","diameter_class"))
mean_k<- dt_fb%>% group_by(growth_form,species,diameter_class,mesh_size)%>% 
  summarise(k_mean= mean(k),
            se= sd(k)/sqrt(n()))
  

big<- mean_k[mean_k$diameter_class %in% c("5.0 cm"), ]
big_crs<- big[big$mesh_size %in% c("Invertebrates access"), ]
big_fine<- big[big$mesh_size %in% c("Invertebrates blocked"), ]

small<- mean_k[mean_k$diameter_class %in% c("2.5 cm"), ]
small_crs<- small[small$mesh_size %in% c("Invertebrates access"), ]
small_fine<- small[small$mesh_size %in% c("Invertebrates blocked"), ]


bg_crs_plot<- ggplot(big_crs, aes(y = species, x= k_mean,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar( data=big_crs, aes(xmin=k_mean-se,xmax=k_mean+se, width=0.5))+  
  scale_x_continuous(limits = c(0,5.0))+
  guides(fill = guide_colourbar(title = "Growth form"))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_blank(),
        axis.text.x =element_text(size=10,colour="black"),
        axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Decomposition rate (k)")+ 
  theme(legend.position = "none")+
  ggtitle(label="
4.0 cm wood - inverterbrates present")+ theme(
  plot.title = element_text(color = "black", size = 12, face = "bold"),
  plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
bg_crs_plot

bg_fine_plot<- ggplot(big_fine, aes(y = species, x= k_mean,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar( data=big_fine, aes(xmin=k_mean-se,xmax=k_mean+se, width=0.5))+  
  scale_x_continuous(limits = c(0,5.0))+
  guides(fill = guide_colourbar(title = "Growth form"))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_blank() ,
        axis.text.x =element_text(size=10,colour="black"),
        axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Decomposition rate (k)")+ 
  theme(legend.position = "none")+
  ggtitle(label="
4.0 cm wood - inverterbrates absent")+ theme(
  plot.title = element_text(color = "black", size = 12, face = "bold"),
  plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
bg_fine_plot

sm_crs_plot<- ggplot(small_crs, aes(y = species, x= k_mean,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar( data=small_crs, aes(xmin=k_mean-se,xmax=k_mean+se, width=0.5))+  
  scale_x_continuous(limits = c(0,5.0))+
  guides(fill = guide_colourbar(title = "Growth form"))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_text(size = 9, face = "italic") ,
        axis.text.x =element_text(size=10,colour="black"),
        axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Decomposition rate (k)")+ 
  theme(legend.position = "none")+
  ggtitle(label="
2.5 cm wood - inverterbrates present")+ theme(
  plot.title = element_text(color = "black", size = 12, face = "bold"),
  plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
sm_crs_plot

sm_fine_plot<- ggplot(small_fine, aes(y = species, x= k_mean,fill= growth_form)) +
  geom_bar(stat = "identity", position = "dodge")+
  geom_errorbar( data=small_fine, aes(xmin=k_mean-se,xmax=k_mean+se, width=0.5))+ 
  scale_x_continuous(limits = c(0,5.0))+
  theme(axis.title.y =element_blank(),
        axis.ticks.y=element_blank(),
        axis.text.y =element_text(size = 9, face = "italic") ,
        axis.text.x =element_text(size=10,colour="black"),
        axis.title.x =element_text(size=12, colour = "black"))+ 
  labs(x= "Decomposition rate (k)", fill= "Growth form")+ 
  theme(legend.position = c(0.8,0.8))+
  ggtitle(label="
2.5 cm wood - inverterbrates absent")+ theme(
  plot.title = element_text(color = "black", size = 12, face = "bold"),
  plot.subtitle = element_text(color = "black",size = 12))+
  theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
sm_fine_plot

k_plot<- sm_fine_plot+bg_fine_plot+sm_crs_plot+bg_crs_plot
k_plot

## Plot liana and tree wood mean decomposition rate per year 
ggsave(filename="Figure S5.png", plot=k_plot, device="png",
       path=path, height=8, width=8, units="in", dpi=500)
library(FactoMineR)
library(factoextra)
library(clusterSim) 
dim(dt_fb)
## [1] 384  36
liana_tree2 <- dt_fb[!apply(dt_fb[,c( 'k',"mesh_size", "diameter_class","block", "mean_density","avg_brkwd_ratio",
                                       "Nitrogen_wood", "Carbon_wood", "cellulose_wood", "hemicellulose_wood",
                                 "Tannins_wood","Calcium_wood","Potassium_wood", "magesium_wood","Manganese_wood",
                                 "Phosphorous_wood","sugars_wood", "Carbon_bark", "Nitrogen_bark",
                                 "cellulose_bark", "hemicellulose_bark","ADL_bark","Tannins_bark","Calcium_bark",
                                 "Potassium_bark", "magesium_bark","Manganese_bark","Phosphorous_bark",
                                 "sugars_bark","Silicon_wood","Silicon_bark")], 1, anyNA),]
dim(liana_tree2) 
## [1] 336  36
liana_tree2$C_N<- liana_tree2$Carbon_wood/liana_tree2$Nitrogen_wood
liana_tree2$lig_N<- liana_tree2$ADL_wood/liana_tree2$Nitrogen_wood
dim(liana_tree2)
## [1] 336  38
pcn<- ungroup(liana_tree2) %>% dplyr::select(Carbon_wood, Nitrogen_wood, cellulose_wood, hemicellulose_wood, ADL_wood,Tannins_wood,Calcium_wood, Potassium_wood,magesium_wood,Manganese_wood,Phosphorous_wood,sugars_wood,Silicon_wood, C_N,lig_N)

dim(pcn)
## [1] 336  15
res<- cor(pcn, method = "pearson")
corrplot::corrplot(res, method = "color",order = "hclust", tl.pos = 'n')

library(ggcorrplot)
# Get the upeper triangle
ggcorrplot(res, hc.order = TRUE, type = "upper",
           outline.col = "white")

ggcorrplot(res, hc.order = TRUE, type = "lower",
           lab = TRUE)

wdpca<- prcomp(pcn, scale=TRUE)
# getting PCA scores
s <- as.data.frame(wdpca$x)
s <- dplyr::select(s, PC1:PC6)
s$mesh_size <- liana_tree2$mesh_size
s$growth_form <- liana_tree2$growth_form
s$density <- liana_tree2$avg_density
s$bark_wd_ratio<- liana_tree2$avg_brkwd_ratio
s$species<- liana_tree2$species
s$k<- liana_tree2$k
s$block<- liana_tree2$block

12 Bark pca

liana_tree2$C_N_bark<- liana_tree2$Carbon_bark/liana_tree2$Nitrogen_bark
liana_tree2$lig_N_bark<- liana_tree2$ADL_bark/liana_tree2$Nitrogen_bark

dim(liana_tree2)
## [1] 336  40
pcnb<- ungroup(liana_tree2) %>% dplyr::select(Carbon_bark, Nitrogen_bark, cellulose_bark, hemicellulose_bark, ADL_bark,Tannins_bark, Calcium_bark,Potassium_bark,magesium_bark,Manganese_wood,Phosphorous_bark,sugars_wood,Silicon_bark,C_N_bark,lig_N_bark)

dim(pcnb)
## [1] 336  15
resb<- cor(pcnb, method = "pearson")
corrplot::corrplot(resb, method = "color",order = "hclust", tl.pos = 'n')

# getting PCA scores for the bark
bakpca <- prcomp(pcnb, scale=TRUE)

s_bark <- as.data.frame(bakpca$x)
s_bark <- dplyr::select(s_bark, PC1:PC6)
s$PC1_bark<- s_bark$PC1
s$PC2_bark<- s_bark$PC2
s$PC3_bark<- s_bark$PC3
s$diameter_class<- liana_tree2$diameter_class
library(FactoMineR)
library(ggplot2)
library(ggrepel)
library(reshape2)
#env=rapply(env,scale,c("numeric"),how="replace")

pca_wd <- PCA(liana_tree2[ ,c("growth_form","C_N","lig_N","ADL_wood", "Tannins_wood", "hemicellulose_wood", "Nitrogen_wood","Carbon_wood","cellulose_wood","Calcium_wood","Potassium_wood","magesium_wood", "Manganese_wood", "Phosphorous_wood", "sugars_wood","Silicon_bark")],scale.unit = TRUE, quali.sup = c(1:1), graph = FALSE)

summary(pca_wd)
## 
## Call:
## PCA(X = liana_tree2[, c("growth_form", "C_N", "lig_N", "ADL_wood",  
##      "Tannins_wood", "hemicellulose_wood", "Nitrogen_wood", "Carbon_wood",  
##      "cellulose_wood", "Calcium_wood", "Potassium_wood", "magesium_wood",  
##      "Manganese_wood", "Phosphorous_wood", "sugars_wood", "Silicon_bark")],  
##      scale.unit = TRUE, quali.sup = c(1:1), graph = FALSE) 
## 
## 
## Eigenvalues
##                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6   Dim.7
## Variance               5.454   2.215   1.857   1.652   1.079   0.771   0.648
## % of var.             36.362  14.770  12.381  11.011   7.193   5.137   4.323
## Cumulative % of var.  36.362  51.132  63.513  74.524  81.717  86.854  91.177
##                        Dim.8   Dim.9  Dim.10  Dim.11  Dim.12  Dim.13  Dim.14
## Variance               0.572   0.347   0.192   0.089   0.050   0.049   0.015
## % of var.              3.812   2.315   1.278   0.591   0.336   0.327   0.100
## Cumulative % of var.  94.989  97.304  98.582  99.174  99.510  99.837  99.937
##                       Dim.15
## Variance               0.009
## % of var.              0.063
## Cumulative % of var. 100.000
## 
## Individuals (the 10 first)
##                        Dist    Dim.1    ctr   cos2    Dim.2    ctr   cos2  
## 1                  |  2.051 | -0.931  0.047  0.206 | -0.574  0.044  0.078 |
## 2                  |  2.051 | -0.931  0.047  0.206 | -0.574  0.044  0.078 |
## 3                  |  2.051 | -0.931  0.047  0.206 | -0.574  0.044  0.078 |
## 4                  |  2.051 | -0.931  0.047  0.206 | -0.574  0.044  0.078 |
## 5                  |  2.051 | -0.931  0.047  0.206 | -0.574  0.044  0.078 |
## 6                  |  2.051 | -0.931  0.047  0.206 | -0.574  0.044  0.078 |
## 7                  |  2.051 | -0.931  0.047  0.206 | -0.574  0.044  0.078 |
## 8                  |  2.051 | -0.931  0.047  0.206 | -0.574  0.044  0.078 |
## 9                  |  2.051 | -0.931  0.047  0.206 | -0.574  0.044  0.078 |
## 10                 |  2.051 | -0.931  0.047  0.206 | -0.574  0.044  0.078 |
##                     Dim.3    ctr   cos2  
## 1                   0.248  0.010  0.015 |
## 2                   0.248  0.010  0.015 |
## 3                   0.248  0.010  0.015 |
## 4                   0.248  0.010  0.015 |
## 5                   0.248  0.010  0.015 |
## 6                   0.248  0.010  0.015 |
## 7                   0.248  0.010  0.015 |
## 8                   0.248  0.010  0.015 |
## 9                   0.248  0.010  0.015 |
## 10                  0.248  0.010  0.015 |
## 
## Variables (the 10 first)
##                       Dim.1    ctr   cos2    Dim.2    ctr   cos2    Dim.3
## C_N                |  0.873 13.983  0.763 | -0.062  0.174  0.004 |  0.141
## lig_N              |  0.857 13.473  0.735 |  0.333  4.993  0.111 |  0.285
## ADL_wood           |  0.402  2.957  0.161 |  0.756 25.807  0.572 |  0.393
## Tannins_wood       | -0.069  0.086  0.005 | -0.157  1.108  0.025 |  0.185
## hemicellulose_wood | -0.378  2.624  0.143 |  0.027  0.034  0.001 | -0.724
## Nitrogen_wood      | -0.750 10.301  0.562 |  0.163  1.198  0.027 | -0.101
## Carbon_wood        |  0.800 11.748  0.641 | -0.016  0.011  0.000 |  0.058
## cellulose_wood     |  0.361  2.389  0.130 | -0.698 21.965  0.487 | -0.040
## Calcium_wood       | -0.629  7.245  0.395 |  0.508 11.626  0.258 | -0.031
## Potassium_wood     | -0.678  8.426  0.460 |  0.023  0.024  0.001 |  0.506
##                       ctr   cos2  
## C_N                 1.066  0.020 |
## lig_N               4.386  0.081 |
## ADL_wood            8.320  0.155 |
## Tannins_wood        1.850  0.034 |
## hemicellulose_wood 28.195  0.524 |
## Nitrogen_wood       0.547  0.010 |
## Carbon_wood         0.181  0.003 |
## cellulose_wood      0.088  0.002 |
## Calcium_wood        0.050  0.001 |
## Potassium_wood     13.764  0.256 |
## 
## Supplementary categories
##                        Dist    Dim.1   cos2 v.test    Dim.2   cos2 v.test  
## Lianas             |  1.459 | -1.120  0.590 -9.209 |  0.763  0.274  9.841 |
## Trees              |  1.605 |  1.232  0.590  9.209 | -0.839  0.274 -9.841 |
##                     Dim.3   cos2 v.test  
## Lianas             -0.380  0.068 -5.354 |
## Trees               0.418  0.068  5.354 |
#plot.PCA(pca_wd)
# extract pc scores for first two component and add to dat dataframe
liana_tree2$pc1_wood <- pca_wd$ind$coord[, 1] # indexing the first column
liana_tree2$pc2_wood <- pca_wd$ind$coord[, 2]  # indexing the second column
pca_vars_wd <- pca_wd$var$coord %>% data.frame
pca_vars_wd $vars <- rownames(pca_vars_wd )
pca_vars_wd_m <- melt(pca_vars_wd, id.vars = "vars")

library(ggplot2)
wd_pcaplot <- ggplot(data = liana_tree2, aes(x = pc1_wood*-1, y = pc2_wood*-1)) +
  geom_hline(yintercept = 0, lty = 2) +
  geom_vline(xintercept = 0, lty = 2) +
  geom_point(alpha = 0.5,size= 3, aes(colour=growth_form)) +scale_shape_manual(values=c(15,19,17))+
  scale_colour_manual(values = c("lightcoral", "#00E5EE"))+
  geom_segment(data = pca_vars_wd, aes(x = 0, xend = Dim.1*5.5*-1, y = 0, yend = Dim.2*5.5*-1),arrow = arrow(length = unit(0.025, "npc"), type = "open"), lwd = 0.7, colour= "gray") + 
  xlab("PC1 36.66%") + ylab("PC2 14.77%") + coord_equal() + theme_minimal() + 
  geom_text_repel(data = pca_vars_wd ,size=2.7, fontface = 'bold', min.segment.length = Inf,max.overlaps = Inf,
    hjust = 0, segment.size = 0.2,aes(x = Dim.1*6.2*-1, y =  Dim.2*6.2*-1,
    label = c("C:N", "Lignin:N","Lignin", "Condensed tannins","Hemicellulose", "N","C","Cellulose","Ca","K","Mg","Mn","P","Sugars","Si")),)+
  theme(legend.position = c(0.8,0.8))+
  theme(text = element_text(size = 10,face="bold"), panel.background = element_blank(),
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5))+
  theme(plot.title = element_text(size = 10))+ labs(colour= "Growth form",title = "Wood traits")


wd_pcaplot

#bark PCA plots 


pca_brk<- PCA(liana_tree2[ ,c("growth_form","C_N","lig_N","ADL_bark", "Tannins_bark", "hemicellulose_bark", "Nitrogen_bark","Carbon_bark","cellulose_bark","Calcium_bark","Potassium_bark","magesium_bark", "Manganese_bark", "Phosphorous_bark", "sugars_bark","Silicon_bark")],scale.unit = TRUE, quali.sup = c(1:1), graph = FALSE)

summary(pca_brk)
## 
## Call:
## PCA(X = liana_tree2[, c("growth_form", "C_N", "lig_N", "ADL_bark",  
##      "Tannins_bark", "hemicellulose_bark", "Nitrogen_bark", "Carbon_bark",  
##      "cellulose_bark", "Calcium_bark", "Potassium_bark", "magesium_bark",  
##      "Manganese_bark", "Phosphorous_bark", "sugars_bark", "Silicon_bark")],  
##      scale.unit = TRUE, quali.sup = c(1:1), graph = FALSE) 
## 
## 
## Eigenvalues
##                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6   Dim.7
## Variance               3.938   3.244   1.832   1.509   1.139   1.083   0.698
## % of var.             26.253  21.625  12.214  10.063   7.595   7.220   4.653
## Cumulative % of var.  26.253  47.878  60.092  70.155  77.750  84.969  89.623
##                        Dim.8   Dim.9  Dim.10  Dim.11  Dim.12  Dim.13  Dim.14
## Variance               0.516   0.350   0.287   0.168   0.126   0.062   0.039
## % of var.              3.437   2.335   1.915   1.117   0.840   0.416   0.260
## Cumulative % of var.  93.060  95.395  97.311  98.428  99.267  99.683  99.943
##                       Dim.15
## Variance               0.009
## % of var.              0.057
## Cumulative % of var. 100.000
## 
## Individuals (the 10 first)
##                       Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3
## 1                  | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## 2                  | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## 3                  | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## 4                  | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## 5                  | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## 6                  | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## 7                  | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## 8                  | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## 9                  | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## 10                 | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
##                      ctr  cos2  
## 1                  0.008 0.002 |
## 2                  0.008 0.002 |
## 3                  0.008 0.002 |
## 4                  0.008 0.002 |
## 5                  0.008 0.002 |
## 6                  0.008 0.002 |
## 7                  0.008 0.002 |
## 8                  0.008 0.002 |
## 9                  0.008 0.002 |
## 10                 0.008 0.002 |
## 
## Variables (the 10 first)
##                       Dim.1    ctr   cos2    Dim.2    ctr   cos2    Dim.3
## C_N                | -0.716 13.024  0.513 |  0.161  0.803  0.026 |  0.236
## lig_N              | -0.728 13.447  0.530 | -0.056  0.098  0.003 |  0.235
## ADL_bark           | -0.103  0.268  0.011 | -0.855 22.510  0.730 | -0.043
## Tannins_bark       | -0.307  2.388  0.094 |  0.808 20.118  0.653 | -0.085
## hemicellulose_bark |  0.064  0.105  0.004 | -0.240  1.771  0.057 |  0.075
## Nitrogen_bark      |  0.676 11.620  0.458 | -0.200  1.234  0.040 |  0.399
## Carbon_bark        | -0.693 12.184  0.480 | -0.195  1.177  0.038 |  0.216
## cellulose_bark     | -0.249  1.576  0.062 |  0.582 10.454  0.339 |  0.020
## Calcium_bark       |  0.368  3.430  0.135 |  0.089  0.242  0.008 | -0.794
## Potassium_bark     |  0.804 16.400  0.646 |  0.079  0.191  0.006 |  0.325
##                       ctr   cos2  
## C_N                 3.033  0.056 |
## lig_N               3.016  0.055 |
## ADL_bark            0.102  0.002 |
## Tannins_bark        0.398  0.007 |
## hemicellulose_bark  0.311  0.006 |
## Nitrogen_bark       8.677  0.159 |
## Carbon_bark         2.547  0.047 |
## cellulose_bark      0.022  0.000 |
## Calcium_bark       34.441  0.631 |
## Potassium_bark      5.770  0.106 |
## 
## Supplementary categories
##                        Dist    Dim.1   cos2 v.test    Dim.2   cos2 v.test  
## Lianas             |  1.416 |  0.838  0.350  8.105 | -0.918  0.421 -9.787 |
## Trees              |  1.558 | -0.922  0.350 -8.105 |  1.010  0.421  9.787 |
##                     Dim.3   cos2 v.test  
## Lianas             -0.517  0.133 -7.337 |
## Trees               0.569  0.133  7.337 |
#plot.PCA(pca_brk)

library(reshape2)
# extract pc scores for first two component and add to dat dataframe
liana_tree2$pc1_bark <- pca_brk$ind$coord[, 1] # indexing the first column
liana_tree2$pc2_bark <- pca_brk$ind$coord[, 2]  # indexing the second column
pca_vars_brk <- pca_brk$var$coord %>% data.frame

pca_vars_brk$vars <- rownames(pca_vars_brk)

pca_vars_brk.m <- melt(pca_vars_brk, id.vars = "vars")

library(ggplot2)
brk_pcaplot <- ggplot(data = liana_tree2, aes(x = pc1_bark, y = pc2_bark)) +
  geom_hline(yintercept = 0, lty = 2) +
  geom_vline(xintercept = 0, lty = 2) +
  geom_point(alpha = 0.5,size= 3, aes(colour=growth_form)) +scale_shape_manual(values=c(15,19,17))+
  scale_colour_manual(values = c("lightcoral", "#00E5EE"))+
  geom_segment(data = pca_vars_brk, aes(x = 0, xend = Dim.1*5.5, y = 0, yend = Dim.2*5.5),arrow = arrow(length = unit(0.025, "npc"), type = "open"), lwd = 0.7,colour= "gray") + 
  xlab("PC1 26.25.0%") + ylab("PC2 21.63%") + coord_equal() + theme_minimal() + 
  geom_text_repel(data = pca_vars_brk,size=2.7, fontface = 'bold', min.segment.length = Inf,max.overlaps = Inf,
                  hjust = 0, segment.size = 0.2,aes(x = Dim.1*6, y =  Dim.2*6,
                                                    label = c("C:N", "Lignin:N","Lignin", "Condensed tannins","Hemicellulose", "N","C","Cellulose","Ca","K","Mg","Mn","P","Sugars","Si")),)+
  theme(legend.position = "none")+
  theme(text = element_text(size = 10,face="bold"), panel.background = element_blank(),
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        axis.line = element_line(colour = "black"),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5))+ labs(title = "Bark traits")+
  theme(plot.title = element_text(size = 10))


brk_pcaplot

pcaplot<- wd_pcaplot+brk_pcaplot+ plot_layout( ncol=2)
pcaplot

ggsave(filename="Figure 3.png", plot=pcaplot, device="png",
       path=path, height=5, width=8, units="in", dpi=500)
s$mesh_size<- as.factor(s$mesh_size)
hist(s$k)

hist(log(s$k)+3.2)

s$k_trans<- log(s$k)+3.2
hist(s$k_trans)

mod2<- lmer(k_trans~ mesh_size+mesh_size:PC1+mesh_size:PC2+ mesh_size:PC1_bark+ 
              mesh_size:PC2_bark+mesh_size:growth_form +mesh_size:diameter_class+ 
              diameter_class+diameter_class:PC2+diameter_class:PC1+growth_form+PC1_bark+
              diameter_class:growth_form+density:mesh_size+bark_wd_ratio:mesh_size+
              PC2_bark+PC1+PC2+density+bark_wd_ratio+ (1|block)+ (1|species),data=s)
anova(mod2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                            Sum Sq Mean Sq NumDF   DenDF F value    Pr(>F)    
## mesh_size                  6.0839  6.0839     1 298.448 31.2531 5.113e-08 ***
## diameter_class             0.0007  0.0007     1 273.182  0.0037  0.951405    
## growth_form                0.0060  0.0060     1  15.641  0.0310  0.862413    
## PC1_bark                   0.0365  0.0365     1  16.904  0.1873  0.670606    
## PC2_bark                   0.1155  0.1155     1  13.890  0.5932  0.454102    
## PC1                        0.2465  0.2465     1  13.958  1.2661  0.279482    
## PC2                        0.1799  0.1799     1  16.090  0.9241  0.350630    
## density                    0.3948  0.3948     1  40.033  2.0279  0.162185    
## bark_wd_ratio              0.0244  0.0244     1  28.413  0.1256  0.725701    
## mesh_size:PC1              0.9246  0.9246     1 298.448  4.7498  0.030083 *  
## mesh_size:PC2              0.1633  0.1633     1 298.448  0.8388  0.360485    
## mesh_size:PC1_bark         1.0315  1.0315     1 298.448  5.2989  0.022027 *  
## mesh_size:PC2_bark         0.8918  0.8918     1 298.448  4.5812  0.033134 *  
## mesh_size:growth_form      0.4577  0.4577     1 298.448  2.3514  0.126228    
## mesh_size:diameter_class   0.1241  0.1241     1 298.448  0.6375  0.425262    
## PC2:diameter_class         0.0029  0.0029     1 306.284  0.0149  0.902805    
## PC1:diameter_class         0.0020  0.0020     1 310.281  0.0103  0.919118    
## growth_form:diameter_class 0.0963  0.0963     1 305.326  0.4949  0.482264    
## mesh_size:density          1.4034  1.4034     1 298.448  7.2096  0.007657 ** 
## mesh_size:bark_wd_ratio    0.0525  0.0525     1 298.448  0.2698  0.603818    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(mod2, oldNames=FALSE)
##                                                            2.5 %       97.5 %
## sd_(Intercept)|species                               0.202862229  0.413429945
## sd_(Intercept)|block                                 0.069381370  0.368359715
## sigma                                                0.399729139  0.467743020
## (Intercept)                                          3.429460701  5.345746438
## mesh_sizeInvertebrates blocked                      -2.665048911 -1.242682744
## diameter_class5.0 cm                                -0.106670721  0.269521750
## growth_formTrees                                    -0.290400060  0.726821624
## PC1_bark                                            -0.082120594  0.112320248
## PC2_bark                                            -0.110840743  0.094978670
## PC1                                                 -0.004112065  0.170923195
## PC2                                                 -0.039206530  0.228779581
## density                                             -4.082266743 -0.527757744
## bark_wd_ratio                                       -0.286270523  0.316618682
## mesh_sizeInvertebrates blocked:PC1                  -0.116698968 -0.007299318
## mesh_sizeInvertebrates blocked:PC2                  -0.131021105  0.046477680
## mesh_sizeInvertebrates blocked:PC1_bark             -0.144528194 -0.012969241
## mesh_sizeInvertebrates blocked:PC2_bark             -0.140524636 -0.007524164
## mesh_sizeInvertebrates blocked:growth_formTrees     -0.580724400  0.065425615
## mesh_sizeInvertebrates blocked:diameter_class5.0 cm -0.266801195  0.110240844
## PC2:diameter_class5.0 cm                            -0.082045580  0.069613297
## PC1:diameter_class5.0 cm                            -0.046480954  0.053467645
## growth_formTrees:diameter_class5.0 cm               -0.349583399  0.164586952
## mesh_sizeInvertebrates blocked:density               0.539789094  3.263129227
## mesh_sizeInvertebrates blocked:bark_wd_ratio        -0.157396111  0.273920525
mod3<- update(mod2,.~.-PC1:diameter_class )
anova(mod3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                            Sum Sq Mean Sq NumDF   DenDF F value   Pr(>F)    
## mesh_size                  6.0839  6.0839     1 299.397 31.3527 4.87e-08 ***
## diameter_class             0.0006  0.0006     1 280.267  0.0030 0.956515    
## growth_form                0.0070  0.0070     1  15.858  0.0358 0.852269    
## PC1_bark                   0.0349  0.0349     1  17.160  0.1800 0.676610    
## PC2_bark                   0.1153  0.1153     1  13.958  0.5941 0.453694    
## PC1                        0.2456  0.2456     1  14.029  1.2659 0.279421    
## PC2                        0.1848  0.1848     1  16.246  0.9523 0.343448    
## density                    0.4080  0.4080     1  41.258  2.1026 0.154611    
## bark_wd_ratio              0.0312  0.0312     1  33.554  0.1608 0.690942    
## mesh_size:PC1              0.9246  0.9246     1 299.397  4.7650 0.029821 *  
## mesh_size:PC2              0.1633  0.1633     1 299.397  0.8415 0.359720    
## mesh_size:PC1_bark         1.0315  1.0315     1 299.397  5.3158 0.021817 *  
## mesh_size:PC2_bark         0.8918  0.8918     1 299.397  4.5958 0.032855 *  
## mesh_size:growth_form      0.4577  0.4577     1 299.397  2.3589 0.125625    
## mesh_size:diameter_class   0.1241  0.1241     1 299.397  0.6395 0.424524    
## diameter_class:PC2         0.0016  0.0016     1 312.600  0.0083 0.927668    
## diameter_class:growth_form 0.1703  0.1703     1 300.439  0.8777 0.349581    
## mesh_size:density          1.4034  1.4034     1 299.397  7.2325 0.007561 ** 
## mesh_size:bark_wd_ratio    0.0525  0.0525     1 299.397  0.2707 0.603242    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(mod3, oldNames=FALSE)
##                                                             2.5 %       97.5 %
## sd_(Intercept)|species                               0.2029712783  0.413734479
## sd_(Intercept)|block                                 0.0693783335  0.368387981
## sigma                                                0.3997250460  0.467740724
## (Intercept)                                          3.4270046559  5.340812978
## mesh_sizeInvertebrates blocked                      -2.6650435517 -1.242688103
## diameter_class5.0 cm                                -0.0838469914  0.257702211
## growth_formTrees                                    -0.2675293399  0.720104590
## PC1_bark                                            -0.0808984465  0.112478464
## PC2_bark                                            -0.1108646954  0.095066038
## PC1                                                  0.0003113026  0.169449210
## PC2                                                 -0.0394148642  0.228650020
## density                                             -4.0852613417 -0.550131466
## bark_wd_ratio                                       -0.2726347664  0.311869049
## mesh_sizeInvertebrates blocked:PC1                  -0.1166985554 -0.007299730
## mesh_sizeInvertebrates blocked:PC2                  -0.1310204361  0.046477011
## mesh_sizeInvertebrates blocked:PC1_bark             -0.1445276981 -0.012969737
## mesh_sizeInvertebrates blocked:PC2_bark             -0.1405241347 -0.007524666
## mesh_sizeInvertebrates blocked:growth_formTrees     -0.5807219649  0.065423180
## mesh_sizeInvertebrates blocked:diameter_class5.0 cm -0.2667997744  0.110239423
## diameter_class5.0 cm:PC2                            -0.0750266579  0.066391320
## diameter_class5.0 cm:growth_formTrees               -0.3128842884  0.107038207
## mesh_sizeInvertebrates blocked:density               0.5397993551  3.263118965
## mesh_sizeInvertebrates blocked:bark_wd_ratio        -0.1573944855  0.273918900
AIC(mod2)
## [1] 559.8641
AIC(mod3)
## [1] 552.4305
mod4<- update(mod3,.~.-diameter_class:PC2)
anova(mod4)
## Type III Analysis of Variance Table with Satterthwaite's method
##                            Sum Sq Mean Sq NumDF   DenDF F value    Pr(>F)    
## mesh_size                  6.0839  6.0839     1 300.360 31.4542 4.633e-08 ***
## diameter_class             0.0005  0.0005     1 285.515  0.0024  0.960926    
## growth_form                0.0076  0.0076     1  15.966  0.0395  0.844894    
## PC1_bark                   0.0337  0.0337     1  17.293  0.1744  0.681369    
## PC2_bark                   0.1150  0.1150     1  13.996  0.5945  0.453515    
## PC1                        0.2447  0.2447     1  14.068  1.2651  0.279514    
## PC2                        0.1883  0.1883     1  16.326  0.9733  0.338259    
## density                    0.4188  0.4188     1  42.187  2.1654  0.148570    
## bark_wd_ratio              0.0366  0.0366     1  37.230  0.1892  0.666097    
## mesh_size:PC1              0.9246  0.9246     1 300.360  4.7804  0.029557 *  
## mesh_size:PC2              0.1633  0.1633     1 300.360  0.8442  0.358942    
## mesh_size:PC1_bark         1.0315  1.0315     1 300.360  5.3330  0.021604 *  
## mesh_size:PC2_bark         0.8918  0.8918     1 300.360  4.6107  0.032573 *  
## mesh_size:growth_form      0.4577  0.4577     1 300.360  2.3665  0.125014    
## mesh_size:diameter_class   0.1241  0.1241     1 300.360  0.6416  0.423774    
## diameter_class:growth_form 0.2308  0.2308     1 309.056  1.1931  0.275549    
## mesh_size:density          1.4034  1.4034     1 300.360  7.2559  0.007464 ** 
## mesh_size:bark_wd_ratio    0.0525  0.0525     1 300.360  0.2716  0.602655    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(mod4, oldNames=FALSE)
##                                                             2.5 %       97.5 %
## sd_(Intercept)|species                               0.2030508479  0.413949234
## sd_(Intercept)|block                                 0.0693755121  0.368406321
## sigma                                                0.3997226607  0.467739611
## (Intercept)                                          3.4255801317  5.339200362
## mesh_sizeInvertebrates blocked                      -2.6650405995 -1.242691056
## diameter_class5.0 cm                                -0.0726070661  0.252797995
## growth_formTrees                                    -0.2550284257  0.717599839
## PC1_bark                                            -0.0800198873  0.112612670
## PC2_bark                                            -0.1108806436  0.095129789
## PC1                                                  0.0002164365  0.169402962
## PC2                                                 -0.0385287035  0.224938951
## density                                             -4.0871738580 -0.568612581
## bark_wd_ratio                                       -0.2637895923  0.309213291
## mesh_sizeInvertebrates blocked:PC1                  -0.1166983284 -0.007299957
## mesh_sizeInvertebrates blocked:PC2                  -0.1310200676  0.046476643
## mesh_sizeInvertebrates blocked:PC1_bark             -0.1445274250 -0.012970010
## mesh_sizeInvertebrates blocked:PC2_bark             -0.1405238586 -0.007524942
## mesh_sizeInvertebrates blocked:growth_formTrees     -0.5807206238  0.065421839
## mesh_sizeInvertebrates blocked:diameter_class5.0 cm -0.2667989915  0.110238641
## diameter_class5.0 cm:growth_formTrees               -0.2962964313  0.078927151
## mesh_sizeInvertebrates blocked:density               0.5398050086  3.263113312
## mesh_sizeInvertebrates blocked:bark_wd_ratio        -0.1573935903  0.273918004
AIC(mod4)
## [1] 545.6784
mod5<- update(mod4,.~.-mesh_size:PC2)
anova(mod5)
## Type III Analysis of Variance Table with Satterthwaite's method
##                            Sum Sq Mean Sq NumDF   DenDF F value   Pr(>F)    
## mesh_size                  6.2167  6.2167     1 301.360 32.1577 3.33e-08 ***
## diameter_class             0.0005  0.0005     1 286.235  0.0024  0.96093    
## growth_form                0.0076  0.0076     1  15.967  0.0396  0.84485    
## PC1_bark                   0.0337  0.0337     1  17.293  0.1744  0.68138    
## PC2_bark                   0.1149  0.1149     1  13.996  0.5945  0.45351    
## PC1                        0.2446  0.2446     1  14.068  1.2651  0.27952    
## PC2                        0.1882  0.1882     1  16.326  0.9734  0.33824    
## density                    0.4187  0.4187     1  42.208  2.1658  0.14853    
## bark_wd_ratio              0.0366  0.0366     1  37.247  0.1894  0.66593    
## mesh_size:PC1              1.0723  1.0723     1 301.360  5.5466  0.01916 *  
## mesh_size:PC1_bark         0.8809  0.8809     1 301.360  4.5566  0.03360 *  
## mesh_size:PC2_bark         0.8036  0.8036     1 301.360  4.1570  0.04234 *  
## mesh_size:growth_form      0.5087  0.5087     1 301.360  2.6316  0.10580    
## mesh_size:diameter_class   0.0995  0.0995     1 301.360  0.5148  0.47361    
## diameter_class:growth_form 0.2308  0.2308     1 310.078  1.1937  0.27544    
## mesh_size:density          1.2701  1.2701     1 301.360  6.5701  0.01086 *  
## mesh_size:bark_wd_ratio    0.1198  0.1198     1 301.360  0.6199  0.43171    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
AIC(mod4)
## [1] 545.6784
AIC(mod5)
## [1] 540.2025
confint(mod5, oldNames=FALSE)
##                                                            2.5 %       97.5 %
## sd_(Intercept)|species                               0.202964935  0.413895602
## sd_(Intercept)|block                                 0.069325928  0.368388276
## sigma                                                0.400284820  0.468397354
## (Intercept)                                          3.369401100  5.264980641
## mesh_sizeInvertebrates blocked                      -2.480593911 -1.166164267
## diameter_class5.0 cm                                -0.076826995  0.248532421
## growth_formTrees                                    -0.248521660  0.723979563
## PC1_bark                                            -0.086486998  0.103606879
## PC2_bark                                            -0.113020684  0.092817177
## PC1                                                  0.002294043  0.171309425
## PC2                                                 -0.052530055  0.196742754
## density                                             -3.907298382 -0.443535542
## bark_wd_ratio                                       -0.276062345  0.294597199
## mesh_sizeInvertebrates blocked:PC1                  -0.120113839 -0.011848851
## mesh_sizeInvertebrates blocked:PC1_bark             -0.120532620 -0.005999656
## mesh_sizeInvertebrates blocked:PC2_bark             -0.135503921 -0.003640302
## mesh_sizeInvertebrates blocked:growth_formTrees     -0.593021377  0.051715347
## mesh_sizeInvertebrates blocked:diameter_class5.0 cm -0.257728978  0.118147607
## diameter_class5.0 cm:growth_formTrees               -0.296571118  0.079171134
## mesh_sizeInvertebrates blocked:density               0.393060789  2.800106136
## mesh_sizeInvertebrates blocked:bark_wd_ratio        -0.123557618  0.293502517
mod6a<- update(mod5,.~.-mesh_size:bark_wd_ratio )
anova(mod6a)
## Type III Analysis of Variance Table with Satterthwaite's method
##                            Sum Sq Mean Sq NumDF   DenDF F value    Pr(>F)    
## mesh_size                  6.2795  6.2795     1 302.359 32.5236 2.802e-08 ***
## diameter_class             0.0005  0.0005     1 286.957  0.0024  0.960948    
## growth_form                0.0076  0.0076     1  15.968  0.0396  0.844759    
## PC1_bark                   0.0337  0.0337     1  17.295  0.1743  0.681422    
## PC2_bark                   0.1148  0.1148     1  13.997  0.5945  0.453512    
## PC1                        0.2443  0.2443     1  14.069  1.2651  0.279514    
## PC2                        0.1880  0.1880     1  16.327  0.9735  0.338196    
## density                    0.4183  0.4183     1  42.255  2.1667  0.148435    
## bark_wd_ratio              0.0367  0.0367     1  37.284  0.1899  0.665512    
## mesh_size:PC1              1.0591  1.0591     1 302.359  5.4854  0.019825 *  
## mesh_size:PC1_bark         1.1684  1.1684     1 302.359  6.0517  0.014453 *  
## mesh_size:PC2_bark         0.7899  0.7899     1 302.359  4.0912  0.043988 *  
## mesh_size:growth_form      0.9129  0.9129     1 302.359  4.7282  0.030446 *  
## mesh_size:diameter_class   0.1177  0.1177     1 302.359  0.6094  0.435615    
## diameter_class:growth_form 0.2307  0.2307     1 311.100  1.1950  0.275174    
## mesh_size:density          1.3462  1.3462     1 302.359  6.9721  0.008709 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(mod6a, oldNames=FALSE)
##                                                            2.5 %       97.5 %
## sd_(Intercept)|species                               0.202901121  0.413857224
## sd_(Intercept)|block                                 0.069289408  0.368372100
## sigma                                                0.400696908  0.468879510
## (Intercept)                                          3.321716898  5.197674839
## mesh_sizeInvertebrates blocked                      -2.302793505 -1.114213002
## diameter_class5.0 cm                                -0.073892910  0.251471675
## growth_formTrees                                    -0.215645837  0.747712621
## PC1_bark                                            -0.082875804  0.106600814
## PC2_bark                                            -0.113334062  0.092515617
## PC1                                                  0.002080089  0.171106891
## PC2                                                 -0.052542165  0.196745046
## density                                             -3.928077024 -0.464206872
## bark_wd_ratio                                       -0.213919816  0.318956416
## mesh_sizeInvertebrates blocked:PC1                  -0.119741770 -0.011384797
## mesh_sizeInvertebrates blocked:PC1_bark             -0.124854175 -0.014902047
## mesh_sizeInvertebrates blocked:PC2_bark             -0.134939883 -0.002975068
## mesh_sizeInvertebrates blocked:growth_formTrees     -0.618466559 -0.035974366
## mesh_sizeInvertebrates blocked:diameter_class5.0 cm -0.263239319  0.111917094
## diameter_class5.0 cm:growth_formTrees               -0.296772451  0.079350009
## mesh_sizeInvertebrates blocked:density               0.437350766  2.838332015
mod6b<- update(mod6a,.~.-mesh_size:diameter_class)
anova(mod6b)
## Type III Analysis of Variance Table with Satterthwaite's method
##                            Sum Sq Mean Sq NumDF   DenDF F value    Pr(>F)    
## mesh_size                  6.1622  6.1622     1 303.358 31.9571 3.637e-08 ***
## diameter_class             0.0005  0.0005     1 287.678  0.0024   0.96096    
## growth_form                0.0076  0.0076     1  15.968  0.0397   0.84466    
## PC1_bark                   0.0336  0.0336     1  17.296  0.1743   0.68146    
## PC2_bark                   0.1146  0.1146     1  13.997  0.5945   0.45351    
## PC1                        0.2439  0.2439     1  14.069  1.2651   0.27951    
## PC2                        0.1878  0.1878     1  16.328  0.9737   0.33815    
## density                    0.4180  0.4180     1  42.303  2.1677   0.14834    
## bark_wd_ratio              0.0367  0.0367     1  37.322  0.1904   0.66509    
## mesh_size:PC1              1.0617  1.0617     1 303.358  5.5058   0.01960 *  
## mesh_size:PC1_bark         1.1346  1.1346     1 303.358  5.8841   0.01586 *  
## mesh_size:PC2_bark         0.7933  0.7933     1 303.358  4.1141   0.04340 *  
## mesh_size:growth_form      0.8761  0.8761     1 303.358  4.5435   0.03385 *  
## diameter_class:growth_form 0.2307  0.2307     1 312.123  1.1963   0.27490    
## mesh_size:density          1.2594  1.2594     1 303.358  6.5313   0.01109 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(mod6b, oldNames=FALSE)
##                                                        2.5 %       97.5 %
## sd_(Intercept)|species                           0.202839208  0.413818376
## sd_(Intercept)|block                             0.069254374  0.368354596
## sigma                                            0.401101115  0.469352444
## (Intercept)                                      3.323495061  5.200086592
## mesh_sizeInvertebrates blocked                  -2.307097796 -1.117466217
## diameter_class5.0 cm                            -0.082099644  0.184015897
## growth_formTrees                                -0.219323488  0.744026515
## PC1_bark                                        -0.083439895  0.106047503
## PC2_bark                                        -0.113268522  0.092597605
## PC1                                              0.002113429  0.171154277
## PC2                                             -0.052553994  0.196747250
## density                                         -3.891505528 -0.430410097
## bark_wd_ratio                                   -0.214105810  0.318936960
## mesh_sizeInvertebrates blocked:PC1              -0.119875245 -0.011409686
## mesh_sizeInvertebrates blocked:PC1_bark         -0.123737853 -0.013811201
## mesh_sizeInvertebrates blocked:PC2_bark         -0.135153245 -0.003057355
## mesh_sizeInvertebrates blocked:growth_formTrees -0.610921786 -0.028962228
## diameter_class5.0 cm:growth_formTrees           -0.296969914  0.079525483
## mesh_sizeInvertebrates blocked:density           0.378420368  2.756186510
mod7<- update(mod6b,.~.-diameter_class:growth_form)
anova(mod7)
## Type III Analysis of Variance Table with Satterthwaite's method
##                       Sum Sq Mean Sq NumDF   DenDF F value   Pr(>F)    
## mesh_size             6.1622  6.1622     1 304.162 31.9511 3.64e-08 ***
## diameter_class        0.0004  0.0004     1 290.742  0.0022  0.96256    
## growth_form           0.0133  0.0133     1  15.819  0.0691  0.79601    
## PC1_bark              0.0241  0.0241     1  17.136  0.1252  0.72781    
## PC2_bark              0.1128  0.1128     1  13.844  0.5849  0.45722    
## PC1                   0.2376  0.2376     1  13.916  1.2320  0.28583    
## PC2                   0.2034  0.2034     1  16.161  1.0546  0.31957    
## density               0.4412  0.4412     1  42.263  2.2875  0.13786    
## bark_wd_ratio         0.0783  0.0783     1  39.386  0.4060  0.52768    
## mesh_size:PC1         1.0617  1.0617     1 304.162  5.5048  0.01961 *  
## mesh_size:PC1_bark    1.1346  1.1346     1 304.162  5.8830  0.01587 *  
## mesh_size:PC2_bark    0.7933  0.7933     1 304.162  4.1134  0.04342 *  
## mesh_size:growth_form 0.8761  0.8761     1 304.162  4.5427  0.03386 *  
## mesh_size:density     1.2594  1.2594     1 304.162  6.5301  0.01109 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(mod7, oldNames=FALSE)
##                                                        2.5 %       97.5 %
## sd_(Intercept)|species                           0.203357354  0.416141999
## sd_(Intercept)|block                             0.069207488  0.368477524
## sigma                                            0.401821989  0.470219886
## (Intercept)                                      3.325475786  5.209260167
## mesh_sizeInvertebrates blocked                  -2.308183175 -1.116380839
## diameter_class5.0 cm                            -0.100449426  0.103215891
## growth_formTrees                                -0.255218598  0.703308480
## PC1_bark                                        -0.080683402  0.109279874
## PC2_bark                                        -0.113536414  0.093116774
## PC1                                              0.001392856  0.171074837
## PC2                                             -0.050012811  0.200090973
## density                                         -3.932034160 -0.463308977
## bark_wd_ratio                                   -0.187974420  0.342570287
## mesh_sizeInvertebrates blocked:PC1              -0.119974205 -0.011310725
## mesh_sizeInvertebrates blocked:PC1_bark         -0.123838146 -0.013710908
## mesh_sizeInvertebrates blocked:PC2_bark         -0.135273764 -0.002936836
## mesh_sizeInvertebrates blocked:growth_formTrees -0.611452745 -0.028431269
## mesh_sizeInvertebrates blocked:density           0.376250979  2.758355900
mod8<- update(mod7,.~.-bark_wd_ratio)
anova(mod8)
## Type III Analysis of Variance Table with Satterthwaite's method
##                       Sum Sq Mean Sq NumDF   DenDF F value    Pr(>F)    
## mesh_size             6.1622  6.1622     1 304.818 31.9147 3.696e-08 ***
## diameter_class        0.0015  0.0015     1 303.758  0.0076   0.93081    
## growth_form           0.0010  0.0010     1  15.259  0.0054   0.94228    
## PC1_bark              0.0632  0.0632     1  16.630  0.3276   0.57475    
## PC2_bark              0.1228  0.1228     1  14.641  0.6360   0.43791    
## PC1                   0.2667  0.2667     1  14.690  1.3814   0.25856    
## PC2                   0.1760  0.1760     1  16.359  0.9117   0.35355    
## density               0.4227  0.4227     1  40.601  2.1891   0.14671    
## mesh_size:PC1         1.0617  1.0617     1 304.818  5.4985   0.01967 *  
## mesh_size:PC1_bark    1.1346  1.1346     1 304.818  5.8763   0.01593 *  
## mesh_size:PC2_bark    0.7933  0.7933     1 304.818  4.1087   0.04353 *  
## mesh_size:growth_form 0.8761  0.8761     1 304.818  4.5375   0.03396 *  
## mesh_size:density     1.2594  1.2594     1 304.818  6.5227   0.01114 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(mod8, oldNames=FALSE)
##                                                        2.5 %       97.5 %
## sd_(Intercept)|species                           0.201981689  0.410569768
## sd_(Intercept)|block                             0.069143353  0.368131240
## sigma                                            0.402241899  0.470651821
## (Intercept)                                      3.466366547  5.242363658
## mesh_sizeInvertebrates blocked                  -2.308765052 -1.115798962
## diameter_class5.0 cm                            -0.103866420  0.095818095
## growth_formTrees                                -0.273083834  0.631127138
## PC1_bark                                        -0.084240923  0.096428980
## PC2_bark                                        -0.113010653  0.091958170
## PC1                                              0.003214338  0.171383346
## PC2                                             -0.054141871  0.188812736
## density                                         -3.847797259 -0.421891020
## mesh_sizeInvertebrates blocked:PC1              -0.120027258 -0.011257672
## mesh_sizeInvertebrates blocked:PC1_bark         -0.123891914 -0.013657140
## mesh_sizeInvertebrates blocked:PC2_bark         -0.135338376 -0.002872228
## mesh_sizeInvertebrates blocked:growth_formTrees -0.611737395 -0.028146619
## mesh_sizeInvertebrates blocked:density           0.375087956  2.759518923
mod9<- update(mod8,.~.-PC2)
anova(mod9)
## Type III Analysis of Variance Table with Satterthwaite's method
##                       Sum Sq Mean Sq NumDF   DenDF F value    Pr(>F)    
## mesh_size             6.1622  6.1622     1 304.866 31.9210 3.685e-08 ***
## diameter_class        0.0099  0.0099     1 308.854  0.0514   0.82076    
## growth_form           0.0228  0.0228     1  17.108  0.1182   0.73515    
## PC1_bark              0.2082  0.2082     1  16.517  1.0786   0.31398    
## PC2_bark              0.1719  0.1719     1  15.584  0.8905   0.35974    
## PC1                   0.3346  0.3346     1  15.578  1.7331   0.20706    
## density               0.2851  0.2851     1  36.499  1.4769   0.23206    
## mesh_size:PC1         1.0617  1.0617     1 304.866  5.4996   0.01966 *  
## mesh_size:PC1_bark    1.1346  1.1346     1 304.866  5.8775   0.01592 *  
## mesh_size:PC2_bark    0.7933  0.7933     1 304.866  4.1095   0.04351 *  
## mesh_size:growth_form 0.8761  0.8761     1 304.866  4.5384   0.03394 *  
## mesh_size:density     1.2594  1.2594     1 304.866  6.5240   0.01113 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
AIC(mod8)
## [1] 524.7686
AIC(mod9)
## [1] 520.2054
mod10<- update(mod9,.~.-diameter_class)
anova(mod10)
## Type III Analysis of Variance Table with Satterthwaite's method
##                       Sum Sq Mean Sq NumDF   DenDF F value    Pr(>F)    
## mesh_size             6.1622  6.1622     1 305.781 32.0126 3.522e-08 ***
## growth_form           0.0276  0.0276     1  17.375  0.1432   0.70969    
## PC1_bark              0.2013  0.2013     1  16.707  1.0459   0.32103    
## PC2_bark              0.1732  0.1732     1  15.677  0.8999   0.35720    
## PC1                   0.3344  0.3344     1  15.670  1.7370   0.20646    
## density               0.3780  0.3780     1  48.416  1.9636   0.16751    
## mesh_size:PC1         1.0617  1.0617     1 305.781  5.5154   0.01949 *  
## mesh_size:PC1_bark    1.1346  1.1346     1 305.781  5.8944   0.01577 *  
## mesh_size:PC2_bark    0.7933  0.7933     1 305.781  4.1213   0.04321 *  
## mesh_size:growth_form 0.8761  0.8761     1 305.781  4.5514   0.03369 *  
## mesh_size:density     1.2594  1.2594     1 305.781  6.5427   0.01101 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
AIC(mod9)
## [1] 520.2054
AIC(mod10)
## [1] 514.1651
AIC(mod2,mod3,mod4,mod5,mod6a,mod6b,mod7,mod8,mod9,mod10)
##       df      AIC
## mod2  24 559.8641
## mod3  23 552.4305
## mod4  22 545.6784
## mod5  21 540.2025
## mod6a 20 536.2074
## mod6b 19 531.9867
## mod7  18 528.3625
## mod8  17 524.7686
## mod9  16 520.2054
## mod10 15 514.1651
r.squaredGLMM(mod10)
##           R2m       R2c
## [1,] 0.508308 0.7168813
#model diagnostics
library(car)
resids <- resid(mod10, type='pearson')
plot(resids~fitted(mod10))

#lines(lowess(resids~fitted(mod10), col='red'))

plot(sqrt(abs(resids))~ fitted(mod10))

#lines(lowess(sqrt(abs(resids))~fitted(mod10)), col='red')     
library(car)
qqPlot(resids)

## [1] 81 37
r.squaredGLMM(mod10)
##           R2m       R2c
## [1,] 0.508308 0.7168813
xm<- anova(mod10)
#write.csv(xm, "xm.csv")
#getwd()

summary(mod10)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: k_trans ~ mesh_size + growth_form + PC1_bark + PC2_bark + PC1 +  
##     density + (1 | block) + (1 | species) + mesh_size:PC1 + mesh_size:PC1_bark +  
##     mesh_size:PC2_bark + mesh_size:growth_form + mesh_size:density
##    Data: s
## 
## REML criterion at convergence: 484.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3585 -0.5833 -0.0308  0.5432  2.9579 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.11728  0.3425  
##  block    (Intercept) 0.02453  0.1566  
##  Residual             0.19249  0.4387  
## Number of obs: 336, groups:  species, 21; block, 4
## 
## Fixed effects:
##                                                  Estimate Std. Error        df
## (Intercept)                                       4.18038    0.43390  55.95652
## mesh_sizeInvertebrates blocked                   -1.71228    0.30656 305.78107
## growth_formTrees                                  0.24964    0.24853  21.00225
## PC1_bark                                         -0.01200    0.04752  20.10330
## PC2_bark                                         -0.01837    0.05832  18.72022
## PC1                                               0.09320    0.04790  18.71113
## density                                          -1.87444    0.83655  62.95366
## mesh_sizeInvertebrates blocked:PC1               -0.06564    0.02795 305.78107
## mesh_sizeInvertebrates blocked:PC1_bark          -0.06877    0.02833 305.78107
## mesh_sizeInvertebrates blocked:PC2_bark          -0.06911    0.03404 305.78107
## mesh_sizeInvertebrates blocked:growth_formTrees  -0.31994    0.14997 305.78107
## mesh_sizeInvertebrates blocked:density            1.56730    0.61274 305.78107
##                                                 t value Pr(>|t|)    
## (Intercept)                                       9.635 1.75e-13 ***
## mesh_sizeInvertebrates blocked                   -5.585 5.15e-08 ***
## growth_formTrees                                  1.004   0.3266    
## PC1_bark                                         -0.253   0.8032    
## PC2_bark                                         -0.315   0.7563    
## PC1                                               1.946   0.0668 .  
## density                                          -2.241   0.0286 *  
## mesh_sizeInvertebrates blocked:PC1               -2.348   0.0195 *  
## mesh_sizeInvertebrates blocked:PC1_bark          -2.428   0.0158 *  
## mesh_sizeInvertebrates blocked:PC2_bark          -2.030   0.0432 *  
## mesh_sizeInvertebrates blocked:growth_formTrees  -2.133   0.0337 *  
## mesh_sizeInvertebrates blocked:density            2.558   0.0110 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) msh_Ib grwt_T PC1_br PC2_br PC1    densty ms_Ib:PC1
## msh_szInvrb -0.353                                                    
## grwth_frmTr  0.083 -0.065                                             
## PC1_bark     0.220 -0.089  0.228                                      
## PC2_bark    -0.169  0.045  0.507 -0.054                               
## PC1         -0.135  0.035  0.421  0.500  0.031                        
## density     -0.931  0.353 -0.347 -0.275  0.030  0.020                 
## msh_sIb:PC1  0.042 -0.120 -0.122 -0.145 -0.009 -0.292 -0.009          
## msh_Ib:PC1_ -0.105  0.297 -0.081 -0.298  0.017 -0.142  0.124  0.486   
## msh_Ib:PC2_  0.054 -0.153 -0.146  0.017 -0.292 -0.009 -0.014  0.031   
## msh_szIb:_T -0.076  0.215 -0.302 -0.080 -0.141 -0.118  0.154  0.403   
## msh_szIblc:  0.341 -0.965  0.127  0.101 -0.011 -0.007 -0.366  0.025   
##             m_Ib:PC1_ m_Ib:PC2 m_Ib:_
## msh_szInvrb                          
## grwth_frmTr                          
## PC1_bark                             
## PC2_bark                             
## PC1                                  
## density                              
## msh_sIb:PC1                          
## msh_Ib:PC1_                          
## msh_Ib:PC2_ -0.057                   
## msh_szIb:_T  0.268     0.485         
## msh_szIblc: -0.338     0.038   -0.422
###
f1<- lmer(k_trans~ mesh_size+mesh_size:growth_form +mesh_size:diameter_class+ 
              diameter_class+diameter_class:growth_form+
            density+bark_wd_ratio+ (1|block)+ (1|species),data=s)
anova(f1)
## Type III Analysis of Variance Table with Satterthwaite's method
##                            Sum Sq Mean Sq NumDF   DenDF  F value Pr(>F)    
## mesh_size                  52.845  52.845     1 306.545 262.4545 <2e-16 ***
## diameter_class              0.001   0.001     1 309.456   0.0068 0.9346    
## density                     0.648   0.648     1  42.031   3.2186 0.0800 .  
## bark_wd_ratio               0.119   0.119     1  44.971   0.5891 0.4468    
## mesh_size:growth_form       0.506   0.253     2  42.920   1.2561 0.2950    
## mesh_size:diameter_class    0.031   0.031     1 306.545   0.1536 0.6953    
## growth_form:diameter_class  0.206   0.206     1 315.728   1.0255 0.3120    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(f1, oldNames=FALSE)
##                                                           2.5 %      97.5 %
## sd_(Intercept)|species                               0.26059343  0.51329515
## sd_(Intercept)|block                                 0.06917762  0.37710062
## sigma                                                0.41189069  0.48196102
## (Intercept)                                          2.94387464  4.83460760
## mesh_sizeInvertebrates blocked                      -1.23939638 -0.91403862
## diameter_class5.0 cm                                -0.09200750  0.24071271
## density                                             -3.16786021  0.09109904
## bark_wd_ratio                                       -0.17299506  0.40415618
## mesh_sizeInvertebrates access:growth_formTrees      -0.28652012  0.59354948
## mesh_sizeInvertebrates blocked:growth_formTrees     -0.15024996  0.72981964
## mesh_sizeInvertebrates blocked:diameter_class5.0 cm -0.22913958  0.15237620
## growth_formTrees:diameter_class5.0 cm               -0.29470869  0.09224962
s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_big<-s_fine[s_fine$diameter_class == "5.0 cm",]
s_fine_big_ln<- s_fine_big[s_fine_big$growth_form == "Lianas",]
s_fine_big_tr<- s_fine_big[s_fine_big$growth_form == "Trees",]

s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_big<-s_fine[s_crs$diameter_class == "5.0 cm",]
s_crs_big_ln<- s_crs_big[s_crs_big$growth_form == "Lianas",]
s_crs_big_tr<- s_crs_big[s_crs_big$growth_form == "Trees",]

hist(s_crs_big_ln$k)

hist(log(s_crs_big_ln$k)+2)

m1<- lmer (log(s_crs_big_ln$k)+2 ~ PC1 + (1|species), data=s_crs_big_ln)
summary(m1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_crs_big_ln$k) + 2 ~ PC1 + (1 | species)
##    Data: s_crs_big_ln
## 
## REML criterion at convergence: 37.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.1915 -0.6116 -0.1103  0.6399  2.0278 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.0242   0.1556  
##  Residual             0.1023   0.3199  
## Number of obs: 44, groups:  species, 11
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)  0.96217    0.08417 9.00000  11.431 1.16e-06 ***
## PC1          0.08074    0.04522 9.00000   1.786    0.108    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##     (Intr)
## PC1 -0.601
anova(m1)
## Type III Analysis of Variance Table with Satterthwaite's method
##      Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC1 0.32632 0.32632     1     9  3.1887 0.1078
confint(m1, oldNames=FALSE)
##                              2.5 %    97.5 %
## sd_(Intercept)|species  0.00000000 0.2871984
## sigma                   0.25587671 0.4155212
## (Intercept)             0.79891811 1.1254278
## PC1                    -0.00695521 0.1684384
m1b<- lm(log(s_crs_big_ln$k)+2 ~ PC1, data=s_crs_big_ln)
anova(m1b)
## Analysis of Variance Table
## 
## Response: log(s_crs_big_ln$k) + 2
##           Df Sum Sq Mean Sq F value  Pr(>F)  
## PC1        1 0.6350 0.63498  5.1591 0.02831 *
## Residuals 42 5.1693 0.12308                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(m1b)
## 
## Call:
## lm(formula = log(s_crs_big_ln$k) + 2 ~ PC1, data = s_crs_big_ln)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.72719 -0.18644 -0.05837  0.24699  0.70468 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.96217    0.06617  14.540   <2e-16 ***
## PC1          0.08074    0.03555   2.271   0.0283 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3508 on 42 degrees of freedom
## Multiple R-squared:  0.1094, Adjusted R-squared:  0.08819 
## F-statistic: 5.159 on 1 and 42 DF,  p-value: 0.02831
hist(s_crs_big_tr$k)

hist(log(s_crs_big_tr$k))

m2<- lmer (k ~ PC1 + (1|species), data=s_crs_big_tr)
summary(m2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: k ~ PC1 + (1 | species)
##    Data: s_crs_big_tr
## 
## REML criterion at convergence: -20.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.7554 -0.5943 -0.1596  0.4306  2.1238 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.02713  0.1647  
##  Residual             0.01751  0.1323  
## Number of obs: 40, groups:  species, 10
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)  0.44957    0.06270 8.00000   7.170 9.52e-05 ***
## PC1          0.01756    0.02269 8.00000   0.774    0.461    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##     (Intr)
## PC1 0.445
anova(m2)
## Type III Analysis of Variance Table with Satterthwaite's method
##       Sum Sq  Mean Sq NumDF DenDF F value Pr(>F)
## PC1 0.010484 0.010484     1     8  0.5988 0.4613
confint(m2, oldNames=FALSE)
##                              2.5 %     97.5 %
## sd_(Intercept)|species  0.08355862 0.25680840
## sigma                   0.10477729 0.17442674
## (Intercept)             0.32820865 0.57093817
## PC1                    -0.02636494 0.06148464
m2b<- lm(k ~ PC1, data=s_crs_big_tr)
summary(m2b)
## 
## Call:
## lm(formula = k ~ PC1, data = s_crs_big_tr)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.26987 -0.14784 -0.02143  0.09259  0.52882 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.44957    0.03548  12.672 3.22e-15 ***
## PC1          0.01756    0.01284   1.368    0.179    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2009 on 38 degrees of freedom
## Multiple R-squared:  0.04691,    Adjusted R-squared:  0.02183 
## F-statistic:  1.87 on 1 and 38 DF,  p-value: 0.1795
anova(m2b)
## Analysis of Variance Table
## 
## Response: k
##           Df  Sum Sq  Mean Sq F value Pr(>F)
## PC1        1 0.07548 0.075478  1.8702 0.1795
## Residuals 38 1.53361 0.040358
hist(log(s_fine_big_ln$k+2))

m3 <- lmer (log(s_fine_big_ln$k+2) ~ PC1+ (1|species), data=s_fine_big_ln)
summary(m3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_big_ln$k + 2) ~ PC1 + (1 | species)
##    Data: s_fine_big_ln
## 
## REML criterion at convergence: -107.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6961 -0.6461 -0.1799  0.5060  2.6002 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.000877 0.02961 
##  Residual             0.003166 0.05627 
## Number of obs: 44, groups:  species, 11
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept) 0.863141   0.015410 9.000000  56.012 9.27e-13 ***
## PC1         0.014698   0.008278 9.000000   1.776     0.11    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##     (Intr)
## PC1 -0.601
anova(m3)
## Type III Analysis of Variance Table with Satterthwaite's method
##        Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)
## PC1 0.0099822 0.0099822     1     9  3.1526 0.1095
confint(m3, oldNames=FALSE)
##                               2.5 %     97.5 %
## sd_(Intercept)|species  0.000000000 0.05317463
## sigma                   0.045008516 0.07315347
## (Intercept)             0.833253167 0.89302862
## PC1                    -0.001357199 0.03075282
hist(log(s_fine_big_tr$k+2))

m3b <- lm (log(s_fine_big_ln$k+2) ~ PC1,data=s_fine_big_ln)
summary(m3b)
## 
## Call:
## lm(formula = log(s_fine_big_ln$k + 2) ~ PC1, data = s_fine_big_ln)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.09845 -0.03696 -0.01526  0.03544  0.16755 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.863141   0.011807  73.105   <2e-16 ***
## PC1         0.014698   0.006342   2.317   0.0254 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06259 on 42 degrees of freedom
## Multiple R-squared:  0.1134, Adjusted R-squared:  0.09226 
## F-statistic:  5.37 on 1 and 42 DF,  p-value: 0.02543
anova(m3b)
## Analysis of Variance Table
## 
## Response: log(s_fine_big_ln$k + 2)
##           Df   Sum Sq   Mean Sq F value  Pr(>F)  
## PC1        1 0.021041 0.0210413  5.3703 0.02543 *
## Residuals 42 0.164558 0.0039181                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m4 <- lmer (log(s_fine_big_tr$k+2) ~ PC1+ (1|species), data=s_fine_big_tr)
summary(m4)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_big_tr$k + 2) ~ PC1 + (1 | species)
##    Data: s_fine_big_tr
## 
## REML criterion at convergence: -91.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6997 -0.6318 -0.1420  0.5202  2.0062 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.004471 0.06686 
##  Residual             0.002691 0.05187 
## Number of obs: 40, groups:  species, 10
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept) 0.893498   0.025331 8.000000  35.273 4.57e-10 ***
## PC1         0.007924   0.009168 8.000000   0.864    0.413    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##     (Intr)
## PC1 0.445
anova(m4)
## Type III Analysis of Variance Table with Satterthwaite's method
##        Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)
## PC1 0.0020099 0.0020099     1     8   0.747 0.4126
confint(m4, oldNames=FALSE)
##                               2.5 %     97.5 %
## sd_(Intercept)|species  0.034476831 0.10395126
## sigma                   0.041074974 0.06837907
## (Intercept)             0.844465176 0.94252991
## PC1                    -0.009822427 0.02566953
m4b <- lm (log(s_fine_big_tr$k+2) ~ PC1, data=s_fine_big_tr)
anova(m4b)
## Analysis of Variance Table
## 
## Response: log(s_fine_big_tr$k + 2)
##           Df   Sum Sq   Mean Sq F value Pr(>F)
## PC1        1 0.015368 0.0153681  2.3806 0.1311
## Residuals 38 0.245309 0.0064555
s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_sm<-s_fine[s_fine$diameter_class == "2.5 cm",]
s_fine_sm_ln<- s_fine_sm[s_fine_sm$growth_form == "Lianas",]
s_fine_sm_tr<- s_fine_sm[s_fine_sm$growth_form == "Trees",]

s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_sm<-s_fine[s_crs$diameter_class == "2.5 cm",]
s_crs_sm_ln<- s_crs_sm[s_crs_sm$growth_form == "Lianas",]
s_crs_sm_tr<- s_crs_sm[s_crs_sm$growth_form == "Trees",]

hist(s_crs_sm_ln$k)

hist(log(s_crs_sm_ln$k)+2)

m5<- lmer (log(s_crs_sm_ln$k)+2 ~ PC1 + (1|species), data=s_crs_sm_ln)
summary(m5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_crs_sm_ln$k) + 2 ~ PC1 + (1 | species)
##    Data: s_crs_sm_ln
## 
## REML criterion at convergence: 37.5
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.08195 -0.60448 -0.05343  0.62368  1.85141 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.20969  0.4579  
##  Residual             0.06716  0.2591  
## Number of obs: 44, groups:  species, 11
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)  1.04847    0.17953 9.00000   5.840 0.000247 ***
## PC1          0.05231    0.09644 9.00000   0.542 0.600689    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##     (Intr)
## PC1 -0.601
anova(m5)
## Type III Analysis of Variance Table with Satterthwaite's method
##      Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC1 0.01976 0.01976     1     9  0.2942 0.6007
confint(m5, oldNames=FALSE)
##                             2.5 %    97.5 %
## sd_(Intercept)|species  0.2674729 0.6871611
## sigma                   0.2072805 0.3368741
## (Intercept)             0.7002658 1.3966724
## PC1                    -0.1347341 0.2393598
m5b<- lm (log(s_crs_sm_ln$k)+2 ~ PC1, data=s_crs_sm_ln)
anova(m5b)
## Analysis of Variance Table
## 
## Response: log(s_crs_sm_ln$k) + 2
##           Df  Sum Sq Mean Sq F value Pr(>F)
## PC1        1  0.2666 0.26655  1.0796 0.3047
## Residuals 42 10.3693 0.24689
hist(s_crs_sm_tr$k)

hist(log(s_crs_sm_tr$k))

m6<- lmer (k ~ PC1 + (1|species), data=s_crs_sm_tr)
summary(m6)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: k ~ PC1 + (1 | species)
##    Data: s_crs_sm_tr
## 
## REML criterion at convergence: -31.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.9270 -0.5776 -0.1539  0.5737  1.7587 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.04646  0.2155  
##  Residual             0.01106  0.1052  
## Number of obs: 40, groups:  species, 10
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)  0.48262    0.07836 8.00000   6.159 0.000271 ***
## PC1          0.01551    0.02836 8.00000   0.547 0.599375    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##     (Intr)
## PC1 0.445
anova(m6)
## Type III Analysis of Variance Table with Satterthwaite's method
##        Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)
## PC1 0.0033078 0.0033078     1     8  0.2991 0.5994
confint(m6, oldNames=FALSE)
##                              2.5 %     97.5 %
## sd_(Intercept)|species  0.12438371 0.32719772
## sigma                   0.08327577 0.13863234
## (Intercept)             0.33092915 0.63430493
## PC1                    -0.03938876 0.07041014
m6b<- lm (k ~ PC1,data=s_crs_sm_tr)
anova(m6b)
## Analysis of Variance Table
## 
## Response: k
##           Df  Sum Sq  Mean Sq F value Pr(>F)
## PC1        1 0.05889 0.058890  1.1735 0.2855
## Residuals 38 1.90699 0.050184
hist((log(s_fine_sm_ln$k+2)))

m7 <- lmer (log(s_fine_sm_ln$k+2) ~ PC1+ (1|species), data=s_fine_sm_ln)
summary(m7)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_sm_ln$k + 2) ~ PC1 + (1 | species)
##    Data: s_fine_sm_ln
## 
## REML criterion at convergence: -105.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.2981 -0.6331 -0.1770  0.4276  2.5887 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.005470 0.07396 
##  Residual             0.002395 0.04894 
## Number of obs: 44, groups:  species, 11
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)  0.88005    0.02939 9.00000  29.946 2.52e-10 ***
## PC1          0.01414    0.01579 9.00000   0.896    0.394    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##     (Intr)
## PC1 -0.601
anova(m7)
## Type III Analysis of Variance Table with Satterthwaite's method
##        Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)
## PC1 0.0019212 0.0019212     1     9  0.8023 0.3937
confint(m7, oldNames=FALSE)
##                              2.5 %     97.5 %
## sd_(Intercept)|species  0.04188612 0.11182343
## sigma                   0.03914139 0.06361291
## (Intercept)             0.82305637 0.93705120
## PC1                    -0.01647763 0.04475781
m7b <- lm(log(s_fine_sm_ln$k+2) ~ PC1, data=s_fine_sm_ln)
summary(m7b)
## 
## Call:
## lm(formula = log(s_fine_sm_ln$k + 2) ~ PC1, data = s_fine_sm_ln)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.16750 -0.04243 -0.01009  0.04211  0.20791 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.880054   0.015875  55.437   <2e-16 ***
## PC1         0.014140   0.008528   1.658    0.105    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.08416 on 42 degrees of freedom
## Multiple R-squared:  0.06144,    Adjusted R-squared:  0.0391 
## F-statistic:  2.75 on 1 and 42 DF,  p-value: 0.1047
anova(m7b)
## Analysis of Variance Table
## 
## Response: log(s_fine_sm_ln$k + 2)
##           Df   Sum Sq   Mean Sq F value Pr(>F)
## PC1        1 0.019475 0.0194747  2.7495 0.1047
## Residuals 42 0.297484 0.0070829
hist(log(s_fine_big_tr$k+2))

m8 <- lmer (log(s_fine_sm_tr$k+2) ~ PC1+ (1|species), data=s_fine_sm_tr)
summary(m8)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_sm_tr$k + 2) ~ PC1 + (1 | species)
##    Data: s_fine_sm_tr
## 
## REML criterion at convergence: -101
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.9315 -0.5454 -0.1325  0.5638  1.6735 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.007420 0.08614 
##  Residual             0.001756 0.04190 
## Number of obs: 40, groups:  species, 10
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept) 0.906591   0.031311 7.999999  28.954 2.19e-09 ***
## PC1         0.007302   0.011332 7.999999   0.644    0.537    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##     (Intr)
## PC1 0.445
anova(m8)
## Type III Analysis of Variance Table with Satterthwaite's method
##         Sum Sq    Mean Sq NumDF DenDF F value Pr(>F)
## PC1 0.00072892 0.00072892     1     8  0.4152 0.5374
confint(m8, oldNames=FALSE)
##                              2.5 %     97.5 %
## sd_(Intercept)|species  0.04972521 0.13074402
## sigma                   0.03317949 0.05523516
## (Intercept)             0.84598246 0.96719875
## PC1                    -0.01463368 0.02923737
m8b <- lm (log(s_fine_sm_tr$k+2) ~ PC1, data=s_fine_sm_tr)
summary(m8b)
## 
## Call:
## lm(formula = log(s_fine_sm_tr$k + 2) ~ PC1, data = s_fine_sm_tr)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.14096 -0.06330 -0.03488  0.07679  0.18893 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.906591   0.015799  57.381   <2e-16 ***
## PC1         0.007302   0.005718   1.277    0.209    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.08946 on 38 degrees of freedom
## Multiple R-squared:  0.04114,    Adjusted R-squared:  0.01591 
## F-statistic: 1.631 on 1 and 38 DF,  p-value: 0.2094
## Bark 

s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_big<-s_fine[s_fine$diameter_class == "5.0 cm",]
s_fine_big_ln<- s_fine_big[s_fine_big$growth_form == "Lianas",]
s_fine_big_tr<- s_fine_big[s_fine_big$growth_form == "Trees",]

s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_big<-s_fine[s_crs$diameter_class == "5.0 cm",]
s_crs_big_ln<- s_crs_big[s_crs_big$growth_form == "Lianas",]
s_crs_big_tr<- s_crs_big[s_crs_big$growth_form == "Trees",]

hist(s_crs_big_ln$k)

hist(log(s_crs_big_ln$k)+2)

b1<- lmer (log(s_crs_big_ln$k)+2 ~ PC1_bark + (1|species), data=s_crs_big_ln)
summary(b1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_crs_big_ln$k) + 2 ~ PC1_bark + (1 | species)
##    Data: s_crs_big_ln
## 
## REML criterion at convergence: 37.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.1002 -0.6450 -0.1749  0.5545  1.9021 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.01783  0.1335  
##  Residual             0.10234  0.3199  
## Number of obs: 44, groups:  species, 11
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  1.03975    0.06309  9.00000  16.482 4.97e-08 ***
## PC1_bark    -0.06424    0.02880  9.00000  -2.231   0.0526 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## PC1_bark 0.091
anova(b1)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF DenDF F value  Pr(>F)  
## PC1_bark 0.50922 0.50922     1     9   4.976 0.05264 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(b1, oldNames=FALSE)
##                             2.5 %       97.5 %
## sd_(Intercept)|species  0.0000000  0.262336003
## sigma                   0.2558767  0.413150896
## (Intercept)             0.9173965  1.162105954
## PC1_bark               -0.1200924 -0.008385257
b1b<- lm(log(s_crs_big_ln$k)+2 ~ PC1_bark, data=s_crs_big_ln)
anova(b1b)
## Analysis of Variance Table
## 
## Response: log(s_crs_big_ln$k) + 2
##           Df Sum Sq Mean Sq F value   Pr(>F)   
## PC1_bark   1 0.8642 0.86418   7.347 0.009686 **
## Residuals 42 4.9401 0.11762                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(b1b)
## 
## Call:
## lm(formula = log(s_crs_big_ln$k) + 2 ~ PC1_bark, data = s_crs_big_ln)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.67079 -0.24635 -0.07185  0.18963  0.66451 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.03975    0.05192  20.027  < 2e-16 ***
## PC1_bark    -0.06424    0.02370  -2.711  0.00969 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.343 on 42 degrees of freedom
## Multiple R-squared:  0.1489, Adjusted R-squared:  0.1286 
## F-statistic: 7.347 on 1 and 42 DF,  p-value: 0.009686
hist(s_crs_big_tr$k)

hist(log(s_crs_big_tr$k))

b2<- lmer (k ~ PC1_bark + (1|species), data=s_crs_big_tr)
summary(b2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: k ~ PC1_bark + (1 | species)
##    Data: s_crs_big_tr
## 
## REML criterion at convergence: -21.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.7606 -0.5788 -0.1482  0.4355  2.1186 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.02616  0.1618  
##  Residual             0.01751  0.1323  
## Number of obs: 40, groups:  species, 10
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  0.43348    0.05558  8.00000   7.799 5.24e-05 ***
## PC1_bark    -0.02525    0.02704  8.00000  -0.934    0.378    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## PC1_bark -0.106
anova(b2)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF DenDF F value Pr(>F)
## PC1_bark 0.015266 0.015266     1     8  0.8719 0.3777
confint(b2, oldNames=FALSE)
##                              2.5 %     97.5 %
## sd_(Intercept)|species  0.08128770 0.25256137
## sigma                   0.10477729 0.17442674
## (Intercept)             0.32589454 0.54106188
## PC1_bark               -0.07758669 0.02709029
b2b<- lm(k ~ PC1_bark, data=s_crs_big_tr)
anova(b2b)
## Analysis of Variance Table
## 
## Response: k
##           Df  Sum Sq  Mean Sq F value Pr(>F)
## PC1_bark   1 0.10652 0.106521  2.6939  0.109
## Residuals 38 1.50257 0.039541
hist(log(s_fine_big_ln$k+2))

b3 <- lmer (log(s_fine_big_ln$k+2) ~ PC1_bark+ (1|species), data=s_fine_big_ln)
summary(b3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_big_ln$k + 2) ~ PC1_bark + (1 | species)
##    Data: s_fine_big_ln
## 
## REML criterion at convergence: -107.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6088 -0.6805 -0.2214  0.4354  2.5817 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev.
##  species  (Intercept) 0.0007327 0.02707 
##  Residual             0.0031664 0.05627 
## Number of obs: 44, groups:  species, 11
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)  0.877362   0.011820  9.000000  74.226  7.4e-14 ***
## PC1_bark    -0.011193   0.005396  9.000000  -2.074   0.0679 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## PC1_bark 0.091
anova(b3)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF DenDF F value  Pr(>F)  
## PC1_bark 0.013625 0.013625     1     9  4.3032 0.06788 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(b3, oldNames=FALSE)
##                              2.5 %        97.5 %
## sd_(Intercept)|species  0.00000000  0.0501748402
## sigma                   0.04500852  0.0730753022
## (Intercept)             0.85443691  0.9002877220
## PC1_bark               -0.02165827 -0.0007278877
b3b <- lm(log(s_fine_big_ln$k+2) ~ PC1_bark, data=s_fine_big_ln)
anova(b3b)
## Analysis of Variance Table
## 
## Response: log(s_fine_big_ln$k + 2)
##           Df   Sum Sq   Mean Sq F value Pr(>F)  
## PC1_bark   1 0.026237 0.0262365  6.9146 0.0119 *
## Residuals 42 0.159363 0.0037944                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(b3b)
## 
## Call:
## lm(formula = log(s_fine_big_ln$k + 2) ~ PC1_bark, data = s_fine_big_ln)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.10378 -0.04586 -0.02014  0.02483  0.16205 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.877362   0.009325   94.09   <2e-16 ***
## PC1_bark    -0.011193   0.004257   -2.63   0.0119 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0616 on 42 degrees of freedom
## Multiple R-squared:  0.1414, Adjusted R-squared:  0.1209 
## F-statistic: 6.915 on 1 and 42 DF,  p-value: 0.0119
hist(log(s_fine_big_tr$k+2))

b4 <- lmer (log(s_fine_big_tr$k+2) ~ PC1_bark+ (1|species), data=s_fine_big_tr)
summary(b4)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_big_tr$k + 2) ~ PC1_bark + (1 | species)
##    Data: s_fine_big_tr
## 
## REML criterion at convergence: -92.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.7064 -0.6015 -0.1244  0.5276  1.9994 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.004305 0.06561 
##  Residual             0.002691 0.05187 
## Number of obs: 40, groups:  species, 10
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  0.88618    0.02244  8.00000  39.494 1.86e-10 ***
## PC1_bark    -0.01112    0.01092  8.00000  -1.019    0.338    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## PC1_bark -0.106
anova(b4)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)
## PC1_bark 0.0027924 0.0027924     1     8  1.0378 0.3381
confint(b4, oldNames=FALSE)
##                              2.5 %     97.5 %
## sd_(Intercept)|species  0.03354177 0.10216041
## sigma                   0.04107497 0.06837907
## (Intercept)             0.84274201 0.92960882
## PC1_bark               -0.03225050 0.01000943
b4b <- lm(log(s_fine_big_tr$k+2) ~ PC1_bark, data=s_fine_big_tr)
anova(b4b)
## Analysis of Variance Table
## 
## Response: log(s_fine_big_tr$k + 2)
##           Df   Sum Sq   Mean Sq F value Pr(>F)  
## PC1_bark   1 0.020665 0.0206646  3.2717 0.0784 .
## Residuals 38 0.240012 0.0063161                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_sm<-s_fine[s_fine$diameter_class == "2.5 cm",]
s_fine_sm_ln<- s_fine_sm[s_fine_sm$growth_form == "Lianas",]
s_fine_sm_tr<- s_fine_sm[s_fine_sm$growth_form == "Trees",]

s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_sm<-s_fine[s_crs$diameter_class == "2.5 cm",]
s_crs_sm_ln<- s_crs_sm[s_crs_sm$growth_form == "Lianas",]
s_crs_sm_tr<- s_crs_sm[s_crs_sm$growth_form == "Trees",]

hist(s_crs_sm_ln$k)

hist(log(s_crs_sm_ln$k)+2)

b5<- lmer (log(s_crs_sm_ln$k)+2 ~ PC1_bark + (1|species), data=s_crs_sm_ln)
summary(b5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_crs_sm_ln$k) + 2 ~ PC1_bark + (1 | species)
##    Data: s_crs_sm_ln
## 
## REML criterion at convergence: 33.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.1983 -0.6303 -0.1213  0.4929  1.9400 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.11224  0.3350  
##  Residual             0.06716  0.2591  
## Number of obs: 44, groups:  species, 11
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  1.08033    0.10875  9.00000   9.934 3.78e-06 ***
## PC1_bark    -0.13426    0.04964  9.00000  -2.704   0.0242 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## PC1_bark 0.091
anova(b5)
## Type III Analysis of Variance Table with Satterthwaite's method
##           Sum Sq Mean Sq NumDF DenDF F value  Pr(>F)  
## PC1_bark 0.49119 0.49119     1     9  7.3141 0.02422 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(b5, oldNames=FALSE)
##                             2.5 %      97.5 %
## sd_(Intercept)|species  0.1809177  0.51171734
## sigma                   0.2072805  0.33687406
## (Intercept)             0.8694090  1.29125625
## PC1_bark               -0.2305437 -0.03797516
b5b<- lm (log(s_crs_sm_ln$k)+2 ~ PC1_bark, data=s_crs_sm_ln)
anova(b5b)
## Analysis of Variance Table
## 
## Response: log(s_crs_sm_ln$k) + 2
##           Df Sum Sq Mean Sq F value    Pr(>F)    
## PC1_bark   1 3.7748  3.7748  23.108 1.987e-05 ***
## Residuals 42 6.8611  0.1634                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(b5b)
## 
## Call:
## lm(formula = log(s_crs_sm_ln$k) + 2 ~ PC1_bark, data = s_crs_sm_ln)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.14699 -0.17848  0.03844  0.18085  0.80117 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.08033    0.06118  17.657  < 2e-16 ***
## PC1_bark    -0.13426    0.02793  -4.807 1.99e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4042 on 42 degrees of freedom
## Multiple R-squared:  0.3549, Adjusted R-squared:  0.3396 
## F-statistic: 23.11 on 1 and 42 DF,  p-value: 1.987e-05
hist(s_crs_sm_tr$k)

hist(log(s_crs_sm_tr$k))

b6<- lmer (k ~ PC1_bark + (1|species), data=s_crs_sm_tr)
summary(b6)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: k ~ PC1_bark + (1 | species)
##    Data: s_crs_sm_tr
## 
## REML criterion at convergence: -31.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.9701 -0.5715 -0.1520  0.5708  1.7646 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.04379  0.2093  
##  Residual             0.01106  0.1052  
## Number of obs: 40, groups:  species, 10
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  0.46995    0.06862  8.00000   6.848 0.000131 ***
## PC1_bark    -0.02938    0.03338  8.00000  -0.880 0.404529    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## PC1_bark -0.106
anova(b6)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)
## PC1_bark 0.0085642 0.0085642     1     8  0.7744 0.4045
confint(b6, oldNames=FALSE)
##                              2.5 %     97.5 %
## sd_(Intercept)|species  0.12030597 0.31797866
## sigma                   0.08327577 0.13863234
## (Intercept)             0.33711417 0.60277777
## PC1_bark               -0.09399896 0.03524399
b6b<- lm (k ~ PC1_bark , data=s_crs_sm_tr)
anova(b6b)
## Analysis of Variance Table
## 
## Response: k
##           Df  Sum Sq  Mean Sq F value  Pr(>F)  
## PC1_bark   1 0.14421 0.144213  3.0083 0.09095 .
## Residuals 38 1.82167 0.047939                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
hist((log(s_fine_sm_ln$k+2)))

b7 <- lmer (log(s_fine_sm_ln$k+2) ~ PC1_bark+ (1|species), data=s_fine_sm_ln)
summary(b7)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_sm_ln$k + 2) ~ PC1_bark + (1 | species)
##    Data: s_fine_sm_ln
## 
## REML criterion at convergence: -109
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.2624 -0.6366 -0.1547  0.2682  2.6553 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.002990 0.05468 
##  Residual             0.002395 0.04894 
## Number of obs: 44, groups:  species, 11
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)  0.891348   0.018136  8.999999  49.147    3e-12 ***
## PC1_bark    -0.022788   0.008279  8.999999  -2.753   0.0224 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## PC1_bark 0.091
anova(b7)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF DenDF F value  Pr(>F)  
## PC1_bark 0.018143 0.018143     1     9  7.5764 0.02238 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(b7, oldNames=FALSE)
##                              2.5 %       97.5 %
## sd_(Intercept)|species  0.02750143  0.084574355
## sigma                   0.03914139  0.063612911
## (Intercept)             0.85617256  0.926523814
## PC1_bark               -0.03884554 -0.006730972
b7b <- lm (log(s_fine_sm_ln$k+2) ~ PC1_bark, data=s_fine_sm_ln)
anova(b7b)
## Analysis of Variance Table
## 
## Response: log(s_fine_sm_ln$k + 2)
##           Df  Sum Sq  Mean Sq F value    Pr(>F)    
## PC1_bark   1 0.10875 0.108750  21.937 2.952e-05 ***
## Residuals 42 0.20821 0.004957                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(b7b)
## 
## Call:
## lm(formula = log(s_fine_sm_ln$k + 2) ~ PC1_bark, data = s_fine_sm_ln)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.14260 -0.03119 -0.00417  0.02615  0.19062 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.891348   0.010658  83.629  < 2e-16 ***
## PC1_bark    -0.022788   0.004865  -4.684 2.95e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07041 on 42 degrees of freedom
## Multiple R-squared:  0.3431, Adjusted R-squared:  0.3275 
## F-statistic: 21.94 on 1 and 42 DF,  p-value: 2.952e-05
hist(log(s_fine_big_tr$k+2))

b8 <- lmer (log(s_fine_sm_tr$k+2) ~ PC1_bark+ (1|species), data=s_fine_sm_tr)
summary(b8)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_sm_tr$k + 2) ~ PC1_bark + (1 | species)
##    Data: s_fine_sm_tr
## 
## REML criterion at convergence: -101.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.9776 -0.5416 -0.1401  0.5680  1.6274 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.006976 0.08352 
##  Residual             0.001756 0.04190 
## Number of obs: 40, groups:  species, 10
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  0.90039    0.02738  8.00000  32.879 7.99e-10 ***
## PC1_bark    -0.01277    0.01332  8.00000  -0.959    0.366    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## PC1_bark -0.106
anova(b8)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)
## PC1_bark 0.0016139 0.0016139     1     8  0.9192 0.3658
confint(b8, oldNames=FALSE)
##                              2.5 %     97.5 %
## sd_(Intercept)|species  0.04802512 0.12689879
## sigma                   0.03317949 0.05523516
## (Intercept)             0.84738650 0.95340285
## PC1_bark               -0.03856108 0.01301492
b8b <- lm (log(s_fine_sm_tr$k+2) ~ PC1_bark, data=s_fine_sm_tr)

anova(b8b)
## Analysis of Variance Table
## 
## Response: log(s_fine_sm_tr$k + 2)
##           Df   Sum Sq   Mean Sq F value  Pr(>F)  
## PC1_bark   1 0.027263 0.0272626  3.5731 0.06637 .
## Residuals 38 0.289935 0.0076299                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

12.1 Plotting

s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_big<-s_fine[s_fine$diameter_class == "5.0 cm",]
lb1 <- expression(paste("", R^2 , "= ", 11.34,"%", "   P = 0.02"))
fine_big_plot<- ggplot()+ geom_point(data=s_fine_big, aes(y=k, x=PC1,color=growth_form,alpha = 0.6), size=3)+ 
  geom_smooth(method = "lm",size=1, data=s_fine_big, aes(y=k, x=PC1, colour=growth_form,linetype=growth_form),se=FALSE)+ 
  scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_linetype_manual (name = "Growth form",values = c("Lianas" = 1, "Trees" = 3))+
  scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_y_continuous(limits = c(0,1.5))+
  labs(colour ="Growth forms", title = "(b) Inverterbrates blocked", y= "Decay rate (k) 4.0 cm WD)", 
       x= "Wood traits PC1")+ 
  annotate("text", x = 0, y = 1.2, size=3, label = as.character(lb1),parse=TRUE, colour= "lightcoral") +
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 10),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank(),
        strip.text = element_text(colour = "black", size = 12),
        legend.direction = "vertical", legend.box = "horizontal",
        legend.position = "none",
        legend.text=element_text(size=10,face="bold.italic"),
        legend.background = element_blank(),
        legend.key.width=unit(0.2,"cm"),
        legend.key.height=unit(0.2,"cm"))+
  guides(fill="none")
fine_big_plot

s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_small<-s_fine[s_fine$diameter_class == "2.5 cm",]
lb1 <- expression(paste("", R^2 , "= ", 35.16,"%", "   P < 0.001"))
lb1b <- expression(paste("", R^2 , "= ", 9.96,"%", "   P = 0.014"))
fine_small_plot<- ggplot()+ geom_point(data=s_fine_small, aes(y=k, x=PC1, colour=growth_form), size=3,alpha = 0.6)+ 
  geom_smooth(method = "lm",size=1, data=s_fine_small, aes(y=k, x=PC1, colour=growth_form, linetype = growth_form),se=FALSE)+
scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 3))+
  scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+scale_y_continuous(limits = c(0,1.5))+
  labs(colour ="Growth forms", title = "(a) Inverterbrates blocked", y= " Decay rate (k) 2.5 cm WD)", 
       x= "Wood traits PC1")+ guides(shape=FALSE)+
  annotate("text", x = 3, y = 10, size=3, label = as.character(lb1),parse=TRUE, colour= "gray60") +
  annotate("text", x = 3, y = 20, size=3, label = as.character(lb1b),parse=TRUE,colour="black") +
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 10),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank(),
        strip.text = element_text(colour = "black", size = 12),
        legend.direction = "vertical", legend.box = "horizontal",
        legend.position = c(0.3,0.8),
        legend.text=element_text(size=10,face="bold.italic"),
        legend.background = element_blank(),
        legend.key.width=unit(0.2,"cm"),
        legend.key.height=unit(0.2,"cm"))+
  guides(fill="none")

fine_small_plot

###
s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_big<-s_crs[s_crs$diameter_class == "5.0 cm",]
lb1 <- expression(paste("", R^2 , "= ", 10.94,"%", "   P = 0.03"))
crs_big_plot<- ggplot()+ geom_point(data=s_crs_big, aes(y=k, x=PC1, colour=growth_form), size=3,alpha = 0.6)+ 
  geom_smooth(method = "lm",size=1, data=s_crs_big, aes(y=k, x=PC1, colour=growth_form,linetype = growth_form),se=FALSE)+ 
  scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_linetype_manual (name = "Growth form",values = c("Lianas" = 1, "Trees" = 3))+
  scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_y_continuous(limits = c(0,5))+
  annotate("text", x = -4, y = 4, size=3, label = as.character(lb1),parse=TRUE, colour= "lightcoral") +
  labs(colour ="Growth forms", title = " (d) Inverterbrates access", y= "Decay rate (k) 4.0 cm WD)", 
       x= "Wood traits PC1")+ guides(shape=FALSE)+
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 10),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank(),
        strip.text = element_text(colour = "black", size = 12),
        legend.direction = "vertical", legend.box = "horizontal",
        legend.position = "none",
        legend.text=element_text(size=10,face="bold.italic"),
        legend.background = element_blank(),
        legend.key.width=unit(0.2,"cm"),
        legend.key.height=unit(0.2,"cm"))+
  guides(fill="none")

crs_big_plot

s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_small<-s_crs[s_crs$diameter_class == "2.5 cm",]
lb1 <- expression(paste("", R^2 , "= ", 35.16,"%", "   P < 0.001"))
lb1b <- expression(paste("", R^2 , "= ", 9.96,"%", "   P = 0.014"))
crs_small_plot<- ggplot()+ geom_point(data=s_crs_small, aes(y=k, x=PC1, colour =growth_form), size=3,alpha = 0.6)+ 
  geom_smooth(method = "lm",size=1, data=s_crs_small, aes(y=k, x=PC1, colour=growth_form, linetype = growth_form),se=FALSE)+ 
  scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 3))+
  scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_y_continuous(limits = c(0,5))+
  labs(colour ="Growth forms", title = "(c) Inverterbrates access", y= " Decay rate (k) 2.5 cm WD)", 
       x= "Wood traits PC1")+ guides(shape=FALSE)+
  annotate("text", x = 3, y = 10, size=3, label = as.character(lb1),parse=TRUE, colour= "gray60") +
  annotate("text", x = 3, y = 20, size=3, label = as.character(lb1b),parse=TRUE,colour="black") +
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 10),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank(),
        strip.text = element_text(colour = "black", size = 12),
        legend.direction = "vertical", legend.box = "horizontal",
        legend.position = "none",
        legend.text=element_text(size=10,face="bold.italic"),
        legend.background = element_blank(),
        legend.key.width=unit(0.2,"cm"),
        legend.key.height=unit(0.2,"cm"))+
  guides(fill="none")
#crs_small_plot

#plt_pca<- fine_small_plot+fine_big_plot+crs_small_plot+crs_big_plot
#ggsave(filename="plt_pca_k.png", plot=plt_pca, device="png",
#       path=path, height=6, width=6, units="in", dpi=500)


###

s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_big <- s_fine[s_fine$diameter_class == "5.0 cm",]
lb1 <- expression(paste("", R^2 , "= ", 14.14,"%", "   P = 0.01"))
fine_big_plot_brk<- ggplot()+ geom_point(data=s_fine_big, aes(y=k, x=PC1_bark*-1,color=growth_form), size=3,alpha = 0.6)+ 
  geom_smooth(method = "lm",size=1, data=s_fine_big, aes(y=k, x=PC1_bark*-1, colour=growth_form,linetype=growth_form),se=FALSE)+ 
  scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_linetype_manual (name = "Growth form",values = c("Lianas" = 1, "Trees" = 3))+
  scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  annotate("text", x = 0, y = 1.2, size=3, label = as.character(lb1),parse=TRUE, colour= "lightcoral") +
  scale_y_continuous(limits = c(0,1.5))+
  labs(colour ="Growth forms", title = "(f) Inverterbrates blocked", y= "Decay rate (k) 4.0 cm WD)", 
       x= "Bark traits PC1")+ guides(shape=FALSE)+
  annotate("text", x = 3, y = 10, size=3, label = as.character(lb1),parse=TRUE, colour= "gray60") +
  annotate("text", x = 3, y = 20, size=3, label = as.character(lb1b),parse=TRUE,colour="black") +
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 10),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank(),
        strip.text = element_text(colour = "black", size = 12),
        legend.direction = "vertical", legend.box = "horizontal",
        legend.position = "none",
        legend.text=element_text(size=10,face="bold.italic"),
        legend.background = element_blank(),
        legend.key.width=unit(0.2,"cm"),
        legend.key.height=unit(0.2,"cm"))+
  guides(fill="none")
fine_big_plot_brk

s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_small<-s_fine[s_fine$diameter_class == "2.5 cm",]

lb1 <- expression(paste("", R^2 , "= ", 34.31,"%", "   P < 0.001"))
fine_small_plot_brk<- ggplot()+ geom_point(data=s_fine_small, aes(y=k, x=PC1_bark*-1, colour=growth_form), size=3,alpha = 0.6)+ 
  geom_smooth(method = "lm",size=1, data=s_fine_small, aes(y=k, x=PC1_bark*-1, colour=growth_form, linetype = growth_form),se=FALSE)+
  scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_linetype_manual (name = "Growth form",values = c("Lianas" = 1, "Trees" = 3))+
  scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+scale_y_continuous(limits = c(0,1.5))+
  annotate("text", x = 0, y = 1.2, size=3, label = as.character(lb1),parse=TRUE, colour= "lightcoral") +
  labs(colour ="Growth forms", title = "(e) Inverterbrates blocked", y= " Decay rate (k) 2.5 cm WD)", 
       x= "Bark traits PC1")+ guides(shape=FALSE)+
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 10),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank(),
        strip.text = element_text(colour = "black", size = 12),
        legend.direction = "vertical", legend.box = "horizontal",
        legend.position = "none",
        legend.text=element_text(size=10,face="bold.italic"),
        legend.background = element_blank(),
        legend.key.width=unit(0.2,"cm"),
        legend.key.height=unit(0.2,"cm"))+
  guides(fill="none")

fine_small_plot_brk

###
s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_big<-s_crs[s_crs$diameter_class == "5.0 cm",]
lb1 <- expression(paste("", R^2 , "= ", 7.92,"%", "   P = 0.01"))
crs_big_plot_brk<- ggplot()+ geom_point(data=s_crs_big, aes(y=k, x=PC1_bark*-1, colour=growth_form), size=3,alpha = 0.6)+ 
  geom_smooth(method = "lm",size=1, data=s_crs_big, aes(y=k, x=PC1_bark*-1, colour=growth_form,linetype = growth_form),se=FALSE)+ 
  scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_linetype_manual (name = "Growth form",values = c("Lianas" = 1, "Trees" = 3))+
  scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  annotate("text", x = 0, y = 4, size=3, label = as.character(lb1),parse=TRUE, colour= "lightcoral") +
  scale_y_continuous(limits = c(0,5))+
  labs(colour ="Growth forms", title = "(h) Inverterbrates access", y= "Decay rate (k) 4.0 cm WD)", 
       x= "Bark traits PC1")+ guides(shape=FALSE)+
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 10),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank(),
        strip.text = element_text(colour = "black", size = 12),
        legend.direction = "vertical", legend.box = "horizontal",
        legend.position = "none",
        legend.text=element_text(size=10,face="bold.italic"),
        legend.background = element_blank(),
        legend.key.width=unit(0.2,"cm"),
        legend.key.height=unit(0.2,"cm"))+
  guides(fill="none")

crs_big_plot_brk

s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_small<-s_crs[s_crs$diameter_class == "2.5 cm",]
lb1 <- expression(paste("", R^2 , "= ", 35.49,"%", "   P < 0.001"))
crs_small_plot_brk<- ggplot()+ geom_point(data=s_crs_small, aes(y=k, x=PC1_bark*-1, colour =growth_form), size=3,alpha = 0.6)+ 
  geom_smooth(method = "lm",size=1, data=s_crs_small, aes(y=k, x=PC1_bark*-1, colour=growth_form, linetype = growth_form),se=FALSE)+ 
  scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_linetype_manual (name = "Growth form",values = c("Lianas" = 1, "Trees" = 3))+
  scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  annotate("text", x = -1.5, y = 4, size=3, label = as.character(lb1),parse=TRUE, colour= "lightcoral") +
  scale_y_continuous(limits = c(0,5))+
  labs(colour ="Growth forms", title = "(g) Inverterbrates access", y= " Decay rate (k) 2.5 cm WD)", 
       x= "Bark traits PC1")+ guides(shape=FALSE)+
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 10),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank(),
        strip.text = element_text(colour = "black", size = 12),
        legend.direction = "vertical", legend.box = "horizontal",
        legend.position = "none",
        legend.text=element_text(size=10,face="bold.italic"),
        legend.background = element_blank(),
        legend.key.width=unit(0.2,"cm"),
        legend.key.height=unit(0.2,"cm"))+
  guides(fill="none")
crs_small_plot_brk

plt_pca_brk<- fine_small_plot_brk+fine_big_plot_brk+crs_small_plot_brk+crs_big_plot_brk
#ggsave(filename="plt_pca_k.png", plot=plt_pca, device="png",
#       path=path, height=6, width=6, units="in", dpi=500)

plt_pca_wd_brk<- fine_small_plot+fine_big_plot+crs_small_plot+crs_big_plot+
  fine_small_plot_brk+fine_big_plot_brk+crs_small_plot_brk+crs_big_plot_brk+plot_layout(nrow = 2)

## Plot figure S7 for wood and bark traits with PC1
ggsave(filename="Figure S7.png", plot=plt_pca_wd_brk, device="png",
       path=path, height=6, width=12, units="in", dpi=500)

12.2 PC2

hist(s_crs_big_ln$k)

hist(log(s_crs_big_ln$k)+2)

m1<- lmer (log(s_crs_big_ln$k)+2 ~ PC2 + (1|species), data=s_crs_big_ln)
summary(m1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_crs_big_ln$k) + 2 ~ PC2 + (1 | species)
##    Data: s_crs_big_ln
## 
## REML criterion at convergence: 39.7
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.16267 -0.75417 -0.00489  0.57337  2.04125 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.04146  0.2036  
##  Residual             0.10234  0.3199  
## Number of obs: 44, groups:  species, 11
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)  1.06461    0.09463 9.00000  11.251 1.33e-06 ***
## PC2          0.01785    0.07889 9.00000   0.226    0.826    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##     (Intr)
## PC2 0.565
anova(m1)
## Type III Analysis of Variance Table with Satterthwaite's method
##        Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)
## PC2 0.0052368 0.0052368     1     9  0.0512 0.8261
confint(m1, oldNames=FALSE)
##                             2.5 %    97.5 %
## sd_(Intercept)|species  0.0000000 0.3458207
## sigma                   0.2558767 0.4158519
## (Intercept)             0.8810801 1.2481326
## PC2                    -0.1351642 0.1708568
m1b<- lm(log(s_crs_big_ln$k)+2 ~ PC2, data=s_crs_big_ln)
anova(m1b)
## Analysis of Variance Table
## 
## Response: log(s_crs_big_ln$k) + 2
##           Df Sum Sq  Mean Sq F value Pr(>F)
## PC2        1 0.0137 0.013723  0.0995  0.754
## Residuals 42 5.7906 0.137871
summary(m1b)
## 
## Call:
## lm(formula = log(s_crs_big_ln$k) + 2 ~ PC2, data = s_crs_big_ln)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.72170 -0.25143 -0.04267  0.22986  0.90193 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.06461    0.06785  15.691   <2e-16 ***
## PC2          0.01785    0.05657   0.315    0.754    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3713 on 42 degrees of freedom
## Multiple R-squared:  0.002364,   Adjusted R-squared:  -0.02139 
## F-statistic: 0.09953 on 1 and 42 DF,  p-value: 0.754
hist(s_crs_big_tr$k)

hist(log(s_crs_big_tr$k))

m2<- lmer (k ~ PC2 + (1|species), data=s_crs_big_tr)
summary(m2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: k ~ PC2 + (1 | species)
##    Data: s_crs_big_tr
## 
## REML criterion at convergence: -22.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.7820 -0.6277 -0.1360  0.4831  2.0972 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.02571  0.1603  
##  Residual             0.01751  0.1323  
## Number of obs: 40, groups:  species, 10
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)  0.40301    0.06022 8.00000   6.692 0.000154 ***
## PC2          0.03346    0.03336 8.00000   1.003 0.345121    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##     (Intr)
## PC2 -0.413
anova(m2)
## Type III Analysis of Variance Table with Satterthwaite's method
##       Sum Sq  Mean Sq NumDF DenDF F value Pr(>F)
## PC2 0.017622 0.017622     1     8  1.0065 0.3451
confint(m2, oldNames=FALSE)
##                              2.5 %     97.5 %
## sd_(Intercept)|species  0.08019561 0.25053900
## sigma                   0.10477729 0.17442674
## (Intercept)             0.28643754 0.51958794
## PC2                    -0.03110173 0.09802825
m2b<- lm(k ~ PC2, data=s_crs_big_tr)
summary(m2b)
## 
## Call:
## lm(formula = k ~ PC2, data = s_crs_big_tr)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.3147 -0.1403 -0.0481  0.1028  0.4916 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.40301    0.03435  11.731 3.37e-14 ***
## PC2          0.03346    0.01903   1.759   0.0867 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1979 on 38 degrees of freedom
## Multiple R-squared:  0.07527,    Adjusted R-squared:  0.05094 
## F-statistic: 3.093 on 1 and 38 DF,  p-value: 0.08667
anova(m2b)
## Analysis of Variance Table
## 
## Response: k
##           Df  Sum Sq  Mean Sq F value  Pr(>F)  
## PC2        1 0.12112 0.121122  3.0932 0.08667 .
## Residuals 38 1.48797 0.039157                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
hist(log(s_fine_big_ln$k+2))

m3 <- lmer (log(s_fine_big_ln$k+2) ~ PC2+ (1|species), data=s_fine_big_ln)
summary(m3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_big_ln$k + 2) ~ PC2 + (1 | species)
##    Data: s_fine_big_ln
## 
## REML criterion at convergence: -105.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6752 -0.7272 -0.1130  0.4444  2.7370 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.001453 0.03812 
##  Residual             0.003166 0.05627 
## Number of obs: 44, groups:  species, 11
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept) 0.881391   0.017314 9.000000  50.906 2.19e-12 ***
## PC2         0.002664   0.014435 9.000000   0.185    0.858    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##     (Intr)
## PC2 0.565
anova(m3)
## Type III Analysis of Variance Table with Satterthwaite's method
##         Sum Sq    Mean Sq NumDF DenDF F value Pr(>F)
## PC2 0.00010788 0.00010788     1     9  0.0341 0.8576
confint(m3, oldNames=FALSE)
##                              2.5 %     97.5 %
## sd_(Intercept)|species  0.00000000 0.06377139
## sigma                   0.04500852 0.07314859
## (Intercept)             0.84781055 0.91497229
## PC2                    -0.02533276 0.03066170
hist(log(s_fine_big_tr$k+2))

m3b <- lm (log(s_fine_big_ln$k+2) ~ PC2,data=s_fine_big_ln)
summary(m3b)
## 
## Call:
## lm(formula = log(s_fine_big_ln$k + 2) ~ PC2, data = s_fine_big_ln)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.09709 -0.04587 -0.01591  0.03182  0.20331 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.881391   0.012137  72.620   <2e-16 ***
## PC2         0.002664   0.010119   0.263    0.794    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06642 on 42 degrees of freedom
## Multiple R-squared:  0.001648,   Adjusted R-squared:  -0.02212 
## F-statistic: 0.06934 on 1 and 42 DF,  p-value: 0.7936
anova(m3b)
## Analysis of Variance Table
## 
## Response: log(s_fine_big_ln$k + 2)
##           Df   Sum Sq   Mean Sq F value Pr(>F)
## PC2        1 0.000306 0.0003059  0.0693 0.7936
## Residuals 42 0.185294 0.0044118
m4 <- lmer (log(s_fine_big_tr$k+2) ~ PC2+ (1|species), data=s_fine_big_tr)
summary(m4)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_big_tr$k + 2) ~ PC2 + (1 | species)
##    Data: s_fine_big_tr
## 
## REML criterion at convergence: -92.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.7300 -0.6498 -0.1169  0.5763  1.9759 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.004243 0.06514 
##  Residual             0.002691 0.05187 
## Number of obs: 40, groups:  species, 10
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)  0.87295    0.02434 8.00000  35.861    4e-10 ***
## PC2          0.01448    0.01348 8.00000   1.074    0.314    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##     (Intr)
## PC2 -0.413
anova(m4)
## Type III Analysis of Variance Table with Satterthwaite's method
##        Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)
## PC2 0.0031023 0.0031023     1     8   1.153 0.3142
confint(m4, oldNames=FALSE)
##                              2.5 %     97.5 %
## sd_(Intercept)|species  0.03318043 0.10147438
## sigma                   0.04107497 0.06837907
## (Intercept)             0.82583259 0.92007192
## PC2                    -0.01162028 0.04057403
m4b <- lm (log(s_fine_big_tr$k+2) ~ PC2, data=s_fine_big_tr)
anova(m4b)
## Analysis of Variance Table
## 
## Response: log(s_fine_big_tr$k + 2)
##           Df   Sum Sq   Mean Sq F value  Pr(>F)  
## PC2        1 0.022669 0.0226692  3.6193 0.06471 .
## Residuals 38 0.238007 0.0062634                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_sm<-s_fine[s_fine$diameter_class == "2.5 cm",]
s_fine_sm_ln<- s_fine_sm[s_fine_sm$growth_form == "Lianas",]
s_fine_sm_tr<- s_fine_sm[s_fine_sm$growth_form == "Trees",]

s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_sm<-s_fine[s_crs$diameter_class == "2.5 cm",]
s_crs_sm_ln<- s_crs_sm[s_crs_sm$growth_form == "Lianas",]
s_crs_sm_tr<- s_crs_sm[s_crs_sm$growth_form == "Trees",]

hist(s_crs_sm_ln$k)

hist(log(s_crs_sm_ln$k)+2)

m5<- lmer (log(s_crs_sm_ln$k)+2 ~ PC2 + (1|species), data=s_crs_sm_ln)
summary(m5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_crs_sm_ln$k) + 2 ~ PC2 + (1 | species)
##    Data: s_crs_sm_ln
## 
## REML criterion at convergence: 36.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.08188 -0.58575 -0.04757  0.61618  1.86929 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.21470  0.4634  
##  Residual             0.06716  0.2591  
## Number of obs: 44, groups:  species, 11
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)  1.13733    0.17583 9.00000   6.468 0.000116 ***
## PC2          0.04476    0.14659 9.00000   0.305 0.767081    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##     (Intr)
## PC2 0.565
anova(m5)
## Type III Analysis of Variance Table with Satterthwaite's method
##        Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)
## PC2 0.0062595 0.0062595     1     9  0.0932 0.7671
confint(m5, oldNames=FALSE)
##                             2.5 %    97.5 %
## sd_(Intercept)|species  0.2711511 0.6949806
## sigma                   0.2072805 0.3368741
## (Intercept)             0.7963078 1.4783599
## PC2                    -0.2395667 0.3290775
m5b<- lm (log(s_crs_sm_ln$k)+2 ~ PC2, data=s_crs_sm_ln)
anova(m5b)
## Analysis of Variance Table
## 
## Response: log(s_crs_sm_ln$k) + 2
##           Df  Sum Sq  Mean Sq F value Pr(>F)
## PC2        1  0.0863 0.086305  0.3436 0.5609
## Residuals 42 10.5496 0.251181
hist(s_crs_sm_tr$k)

hist(log(s_crs_sm_tr$k))

m6<- lmer (k ~ PC2 + (1|species), data=s_crs_sm_tr)
summary(m6)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: k ~ PC2 + (1 | species)
##    Data: s_crs_sm_tr
## 
## REML criterion at convergence: -32.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.9327 -0.6113 -0.1780  0.5863  1.7615 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.04342  0.2084  
##  Residual             0.01106  0.1052  
## Number of obs: 40, groups:  species, 10
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)  0.43520    0.07462 8.00000   5.832 0.000391 ***
## PC2          0.03800    0.04133 8.00000   0.919 0.384773    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##     (Intr)
## PC2 -0.413
anova(m6)
## Type III Analysis of Variance Table with Satterthwaite's method
##        Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)
## PC2 0.0093485 0.0093485     1     8  0.8453 0.3848
confint(m6, oldNames=FALSE)
##                              2.5 %    97.5 %
## sd_(Intercept)|species  0.11972373 0.3166665
## sigma                   0.08327577 0.1386323
## (Intercept)             0.29076041 0.5796369
## PC2                    -0.04200065 0.1179932
m6b<- lm (k ~ PC2,data=s_crs_sm_tr)
anova(m6b)
## Analysis of Variance Table
## 
## Response: k
##           Df  Sum Sq  Mean Sq F value  Pr(>F)  
## PC2        1 0.15616 0.156160   3.279 0.07808 .
## Residuals 38 1.80972 0.047624                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
hist((log(s_fine_sm_ln$k+2)))

m7 <- lmer (log(s_fine_sm_ln$k+2) ~ PC2+ (1|species), data=s_fine_sm_ln)
summary(m7)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_sm_ln$k + 2) ~ PC2 + (1 | species)
##    Data: s_fine_sm_ln
## 
## REML criterion at convergence: -105.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.3079 -0.6337 -0.1672  0.4170  2.5789 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.005984 0.07736 
##  Residual             0.002395 0.04894 
## Number of obs: 44, groups:  species, 11
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept) 0.899079   0.029650 8.999999  30.323 2.26e-10 ***
## PC2         0.004729   0.024720 8.999999   0.191    0.853    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##     (Intr)
## PC2 0.565
anova(m7)
## Type III Analysis of Variance Table with Satterthwaite's method
##         Sum Sq    Mean Sq NumDF DenDF F value Pr(>F)
## PC2 8.7622e-05 8.7622e-05     1     9  0.0366 0.8525
confint(m7, oldNames=FALSE)
##                              2.5 %     97.5 %
## sd_(Intercept)|species  0.04426066 0.11668043
## sigma                   0.03914139 0.06361291
## (Intercept)             0.84157206 0.95658677
## PC2                    -0.04321666 0.05267404
m7b <- lm(log(s_fine_sm_ln$k+2) ~ PC2, data=s_fine_sm_ln)
summary(m7b)
## 
## Call:
## lm(formula = log(s_fine_sm_ln$k + 2) ~ PC2, data = s_fine_sm_ln)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.147521 -0.061555 -0.009852  0.036437  0.232543 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.899079   0.015850  56.725   <2e-16 ***
## PC2         0.004729   0.013214   0.358    0.722    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.08674 on 42 degrees of freedom
## Multiple R-squared:  0.00304,    Adjusted R-squared:  -0.0207 
## F-statistic: 0.1281 on 1 and 42 DF,  p-value: 0.7223
anova(m7b)
## Analysis of Variance Table
## 
## Response: log(s_fine_sm_ln$k + 2)
##           Df   Sum Sq   Mean Sq F value Pr(>F)
## PC2        1 0.000963 0.0009634  0.1281 0.7223
## Residuals 42 0.315995 0.0075237
hist(log(s_fine_big_tr$k+2))

m8 <- lmer (log(s_fine_sm_tr$k+2) ~ PC2+ (1|species), data=s_fine_sm_tr)
summary(m8)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_sm_tr$k + 2) ~ PC2 + (1 | species)
##    Data: s_fine_sm_tr
## 
## REML criterion at convergence: -102.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.9368 -0.5828 -0.1616  0.5779  1.6682 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.006923 0.08321 
##  Residual             0.001756 0.04190 
## Number of obs: 40, groups:  species, 10
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)  0.88541    0.02979 8.00000  29.719 1.78e-09 ***
## PC2          0.01635    0.01650 8.00000   0.991    0.351    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##     (Intr)
## PC2 -0.413
anova(m8)
## Type III Analysis of Variance Table with Satterthwaite's method
##        Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)
## PC2 0.0017248 0.0017248     1     8  0.9824 0.3506
confint(m8, oldNames=FALSE)
##                              2.5 %     97.5 %
## sd_(Intercept)|species  0.04782143 0.12643956
## sigma                   0.03317949 0.05523516
## (Intercept)             0.82774197 0.94307890
## PC2                    -0.01558490 0.04829427
m8b <- lm (log(s_fine_sm_tr$k+2) ~ PC2, data=s_fine_sm_tr)
summary(m8b)
## 
## Call:
## lm(formula = log(s_fine_sm_tr$k + 2) ~ PC2, data = s_fine_sm_tr)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.14553 -0.06887 -0.01528  0.07326  0.18178 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.885410   0.015121  58.556   <2e-16 ***
## PC2         0.016355   0.008375   1.953   0.0582 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0871 on 38 degrees of freedom
## Multiple R-squared:  0.09121,    Adjusted R-squared:  0.06729 
## F-statistic: 3.814 on 1 and 38 DF,  p-value: 0.05822
###

hist(s_crs_big_ln$k)

hist(log(s_crs_big_ln$k)+2)

b1<- lmer (log(s_crs_big_ln$k)+2 ~ PC2_bark + (1|species), data=s_crs_big_ln)
summary(b1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_crs_big_ln$k) + 2 ~ PC2_bark + (1 | species)
##    Data: s_crs_big_ln
## 
## REML criterion at convergence: 40.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.1519 -0.7420 -0.0156  0.5978  2.0459 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.04172  0.2043  
##  Residual             0.10234  0.3199  
## Number of obs: 44, groups:  species, 11
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)  1.060545   0.101315  9.000000  10.468 2.44e-06 ***
## PC2_bark    -0.007902   0.063321  9.000000  -0.125    0.903    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## PC2_bark -0.636
anova(b1)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)
## PC2_bark 0.0015937 0.0015937     1     9  0.0156 0.9034
confint(b1, oldNames=FALSE)
##                             2.5 %    97.5 %
## sd_(Intercept)|species  0.0000000 0.3466436
## sigma                   0.2558767 0.4158524
## (Intercept)             0.8640442 1.2570466
## PC2_bark               -0.1307143 0.1149105
b1b<- lm(log(s_crs_big_ln$k)+2 ~ PC2_bark, data=s_crs_big_ln)
anova(b1b)
## Analysis of Variance Table
## 
## Response: log(s_crs_big_ln$k) + 2
##           Df Sum Sq  Mean Sq F value Pr(>F)
## PC2_bark   1 0.0042 0.004193  0.0304 0.8625
## Residuals 42 5.8001 0.138098
summary(b8b)
## 
## Call:
## lm(formula = log(s_fine_sm_tr$k + 2) ~ PC1_bark, data = s_fine_sm_tr)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.13071 -0.07394 -0.02066  0.07216  0.18521 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.900395   0.013890   64.82   <2e-16 ***
## PC1_bark    -0.012773   0.006757   -1.89   0.0664 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.08735 on 38 degrees of freedom
## Multiple R-squared:  0.08595,    Adjusted R-squared:  0.06189 
## F-statistic: 3.573 on 1 and 38 DF,  p-value: 0.06637
hist(s_crs_big_tr$k)

hist(log(s_crs_big_tr$k))

b2<- lmer (k ~ PC2_bark + (1|species), data=s_crs_big_tr)
summary(b2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: k ~ PC2_bark + (1 | species)
##    Data: s_crs_big_tr
## 
## REML criterion at convergence: -24.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6831 -0.5714 -0.1628  0.4073  2.1960 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.01750  0.1323  
##  Residual             0.01751  0.1323  
## Number of obs: 40, groups:  species, 10
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  0.35877    0.05727  8.00000   6.265 0.000242 ***
## PC2_bark    -0.06186    0.02954  8.00000  -2.094 0.069575 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## PC2_bark 0.577
anova(b2)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF DenDF F value  Pr(>F)  
## PC2_bark 0.076776 0.076776     1     8  4.3852 0.06957 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(b2, oldNames=FALSE)
##                              2.5 %       97.5 %
## sd_(Intercept)|species  0.05666082  0.210881931
## sigma                   0.10477729  0.174426743
## (Intercept)             0.24791832  0.469621207
## PC2_bark               -0.11903836 -0.004679129
b2b<- lm(k ~ PC2_bark, data=s_crs_big_tr)
anova(b2b)
## Analysis of Variance Table
## 
## Response: k
##           Df  Sum Sq Mean Sq F value   Pr(>F)   
## PC2_bark   1 0.38376 0.38376  11.901 0.001388 **
## Residuals 38 1.22534 0.03225                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(b2b)
## 
## Call:
## lm(formula = k ~ PC2_bark, data = s_crs_big_tr)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.25910 -0.12560 -0.02392  0.09191  0.48863 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.35877    0.03476   10.32 1.41e-12 ***
## PC2_bark    -0.06186    0.01793   -3.45  0.00139 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1796 on 38 degrees of freedom
## Multiple R-squared:  0.2385, Adjusted R-squared:  0.2185 
## F-statistic:  11.9 on 1 and 38 DF,  p-value: 0.001388
hist(log(s_fine_big_ln$k+2))

b3 <- lmer (log(s_fine_big_ln$k+2) ~ PC2_bark+ (1|species), data=s_fine_big_ln)
summary(b3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_big_ln$k + 2) ~ PC2_bark + (1 | species)
##    Data: s_fine_big_ln
## 
## REML criterion at convergence: -105.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6663 -0.7256 -0.1201  0.4657  2.7378 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.001460 0.03821 
##  Residual             0.003166 0.05627 
## Number of obs: 44, groups:  species, 11
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)  0.8804219  0.0185315  9.0000000  47.509 4.06e-12 ***
## PC2_bark    -0.0008226  0.0115821  9.0000000  -0.071    0.945    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## PC2_bark -0.636
anova(b3)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq    Mean Sq NumDF DenDF F value Pr(>F)
## PC2_bark 1.5972e-05 1.5972e-05     1     9   0.005 0.9449
confint(b3, oldNames=FALSE)
##                              2.5 %     97.5 %
## sd_(Intercept)|species  0.00000000 0.06389343
## sigma                   0.04500852 0.07314857
## (Intercept)             0.84447994 0.91636391
## PC2_bark               -0.02328618 0.02164100
b3b <- lm(log(s_fine_big_ln$k+2) ~ PC2_bark, data=s_fine_big_ln)
anova(b3b)
## Analysis of Variance Table
## 
## Response: log(s_fine_big_ln$k + 2)
##           Df   Sum Sq   Mean Sq F value Pr(>F)
## PC2_bark   1 0.000045 0.0000454  0.0103 0.9197
## Residuals 42 0.185554 0.0044180
summary(b3b)
## 
## Call:
## lm(formula = log(s_fine_big_ln$k + 2) ~ PC2_bark, data = s_fine_big_ln)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.09568 -0.04591 -0.01463  0.03206  0.20369 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.8804219  0.0129786  67.837   <2e-16 ***
## PC2_bark    -0.0008226  0.0081116  -0.101     0.92    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06647 on 42 degrees of freedom
## Multiple R-squared:  0.0002448,  Adjusted R-squared:  -0.02356 
## F-statistic: 0.01028 on 1 and 42 DF,  p-value: 0.9197
hist(log(s_fine_big_tr$k+2))

b4 <- lmer (log(s_fine_big_tr$k+2) ~ PC2_bark+ (1|species), data=s_fine_big_tr)
summary(b4)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_big_tr$k + 2) ~ PC2_bark + (1 | species)
##    Data: s_fine_big_tr
## 
## REML criterion at convergence: -95.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6456 -0.6114 -0.1353  0.4447  2.0602 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.002997 0.05474 
##  Residual             0.002691 0.05187 
## Number of obs: 40, groups:  species, 10
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  0.85581    0.02345  8.00000  36.491 3.49e-10 ***
## PC2_bark    -0.02497    0.01210  8.00000  -2.064   0.0729 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## PC2_bark 0.577
anova(b4)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF DenDF F value  Pr(>F)  
## PC2_bark 0.011466 0.011466     1     8  4.2613 0.07287 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(b4, oldNames=FALSE)
##                              2.5 %       97.5 %
## sd_(Intercept)|species  0.02480846  0.086710674
## sigma                   0.04107497  0.068379073
## (Intercept)             0.81041610  0.901209676
## PC2_bark               -0.04838904 -0.001555721
b4b <- lm(log(s_fine_big_tr$k+2) ~ PC2_bark, data=s_fine_big_tr)
anova(b4b)
## Analysis of Variance Table
## 
## Response: log(s_fine_big_tr$k + 2)
##           Df   Sum Sq  Mean Sq F value   Pr(>F)   
## PC2_bark   1 0.062542 0.062542  11.995 0.001336 **
## Residuals 38 0.198135 0.005214                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(b4b)
## 
## Call:
## lm(formula = log(s_fine_big_tr$k + 2) ~ PC2_bark, data = s_fine_big_tr)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.111608 -0.051016 -0.006749  0.040043  0.185531 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.85581    0.01398  61.223  < 2e-16 ***
## PC2_bark    -0.02497    0.00721  -3.463  0.00134 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07221 on 38 degrees of freedom
## Multiple R-squared:  0.2399, Adjusted R-squared:  0.2199 
## F-statistic: 11.99 on 1 and 38 DF,  p-value: 0.001336
s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_sm<-s_fine[s_fine$diameter_class == "2.5 cm",]
s_fine_sm_ln<- s_fine_sm[s_fine_sm$growth_form == "Lianas",]
s_fine_sm_tr<- s_fine_sm[s_fine_sm$growth_form == "Trees",]

s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_sm<-s_fine[s_crs$diameter_class == "2.5 cm",]
s_crs_sm_ln<- s_crs_sm[s_crs_sm$growth_form == "Lianas",]
s_crs_sm_tr<- s_crs_sm[s_crs_sm$growth_form == "Trees",]

hist(s_crs_sm_ln$k)

hist(log(s_crs_sm_ln$k)+2)

b5<- lmer (log(s_crs_sm_ln$k)+2 ~ PC2_bark + (1|species), data=s_crs_sm_ln)
summary(b5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_crs_sm_ln$k) + 2 ~ PC2_bark + (1 | species)
##    Data: s_crs_sm_ln
## 
## REML criterion at convergence: 37.5
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.07220 -0.58898 -0.05048  0.62801  1.85966 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.21709  0.4659  
##  Residual             0.06716  0.2591  
## Number of obs: 44, groups:  species, 11
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)  1.1075672  0.1888618  8.9999993   5.864 0.000239 ***
## PC2_bark    -0.0005588  0.1180378  8.9999993  -0.005 0.996326    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## PC2_bark -0.636
anova(b5)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq    Mean Sq NumDF DenDF F value Pr(>F)
## PC2_bark 1.5051e-06 1.5051e-06     1     9       0 0.9963
confint(b5, oldNames=FALSE)
##                             2.5 %    97.5 %
## sd_(Intercept)|species  0.2728939 0.6986928
## sigma                   0.2072805 0.3368741
## (Intercept)             0.7412683 1.4738662
## PC2_bark               -0.2294941 0.2283765
b5b<- lm (log(s_crs_sm_ln$k)+2 ~ PC2_bark, data=s_crs_sm_ln)
anova(b5b)
## Analysis of Variance Table
## 
## Response: log(s_crs_sm_ln$k) + 2
##           Df Sum Sq  Mean Sq F value Pr(>F)
## PC2_bark   1  0.000 0.000021   1e-04 0.9928
## Residuals 42 10.636 0.253235
summary(b5b)
## 
## Call:
## lm(formula = log(s_crs_sm_ln$k) + 2 ~ PC2_bark, data = s_crs_sm_ln)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.23097 -0.31997  0.05289  0.29591  0.97730 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.1075672  0.0982604  11.272 2.79e-14 ***
## PC2_bark    -0.0005588  0.0614123  -0.009    0.993    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5032 on 42 degrees of freedom
## Multiple R-squared:  1.971e-06,  Adjusted R-squared:  -0.02381 
## F-statistic: 8.28e-05 on 1 and 42 DF,  p-value: 0.9928
hist(s_crs_sm_tr$k)

hist(log(s_crs_sm_tr$k))

b6<- lmer (k ~ PC2_bark + (1|species), data=s_crs_sm_tr)
summary(b6)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: k ~ PC2_bark + (1 | species)
##    Data: s_crs_sm_tr
## 
## REML criterion at convergence: -35.4
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.81571 -0.56402 -0.07279  0.57925  1.83611 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.02921  0.1709  
##  Residual             0.01106  0.1052  
## Number of obs: 40, groups:  species, 10
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  0.37623    0.06923  8.00000   5.434  0.00062 ***
## PC2_bark    -0.07804    0.03571  8.00000  -2.185  0.06036 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## PC2_bark 0.577
anova(b6)
## Type III Analysis of Variance Table with Satterthwaite's method
##            Sum Sq  Mean Sq NumDF DenDF F value  Pr(>F)  
## PC2_bark 0.052816 0.052816     1     8  4.7756 0.06036 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(b6, oldNames=FALSE)
##                              2.5 %      97.5 %
## sd_(Intercept)|species  0.09486662  0.26188408
## sigma                   0.08327577  0.13863234
## (Intercept)             0.24221620  0.51024783
## PC2_bark               -0.14717167 -0.00891506
b6b<- lm (k ~ PC2_bark , data=s_crs_sm_tr)
anova(b6b)
## Analysis of Variance Table
## 
## Response: k
##           Df  Sum Sq Mean Sq F value    Pr(>F)    
## PC2_bark   1 0.61084 0.61084   17.13 0.0001866 ***
## Residuals 38 1.35505 0.03566                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(b6b)
## 
## Call:
## lm(formula = k ~ PC2_bark, data = s_crs_sm_tr)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.33234 -0.13523 -0.01884  0.12441  0.41205 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.37623    0.03656  10.292 1.53e-12 ***
## PC2_bark    -0.07804    0.01886  -4.139 0.000187 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1888 on 38 degrees of freedom
## Multiple R-squared:  0.3107, Adjusted R-squared:  0.2926 
## F-statistic: 17.13 on 1 and 38 DF,  p-value: 0.0001866
hist((log(s_fine_sm_ln$k+2)))

b7 <- lmer (log(s_fine_sm_ln$k+2) ~ PC2_bark+ (1|species), data=s_fine_sm_ln)
summary(b7)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_sm_ln$k + 2) ~ PC2_bark + (1 | species)
##    Data: s_fine_sm_ln
## 
## REML criterion at convergence: -104.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.3037 -0.6361 -0.1678  0.4249  2.5831 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.006002 0.07747 
##  Residual             0.002395 0.04894 
## Number of obs: 44, groups:  species, 11
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept) 0.893660   0.031727 8.999999   28.17 4.36e-10 ***
## PC2_bark    0.002177   0.019829 8.999999    0.11    0.915    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## PC2_bark -0.636
anova(b7)
## Type III Analysis of Variance Table with Satterthwaite's method
##              Sum Sq    Mean Sq NumDF DenDF F value Pr(>F)
## PC2_bark 2.8868e-05 2.8868e-05     1     9  0.0121  0.915
confint(b7, oldNames=FALSE)
##                              2.5 %     97.5 %
## sd_(Intercept)|species  0.04434103 0.11684607
## sigma                   0.03914139 0.06361291
## (Intercept)             0.83212500 0.95519582
## PC2_bark               -0.03628215 0.04063659
b7b <- lm (log(s_fine_sm_ln$k+2) ~ PC2_bark, data=s_fine_sm_ln)
anova(b7b)
## Analysis of Variance Table
## 
## Response: log(s_fine_sm_ln$k + 2)
##           Df  Sum Sq   Mean Sq F value Pr(>F)
## PC2_bark   1 0.00032 0.0003183  0.0422 0.8382
## Residuals 42 0.31664 0.0075390
summary(b7b)
## 
## Call:
## lm(formula = log(s_fine_sm_ln$k + 2) ~ PC2_bark, data = s_fine_sm_ln)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.144141 -0.065180 -0.008046  0.041685  0.228663 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.893660   0.016954  52.711   <2e-16 ***
## PC2_bark    0.002177   0.010596   0.205    0.838    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.08683 on 42 degrees of freedom
## Multiple R-squared:  0.001004,   Adjusted R-squared:  -0.02278 
## F-statistic: 0.04222 on 1 and 42 DF,  p-value: 0.8382
hist(log(s_fine_big_tr$k+2))

b8 <- lmer (log(s_fine_sm_tr$k+2) ~ PC2_bark+ (1|species), data=s_fine_sm_tr)
summary(b8)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_sm_tr$k + 2) ~ PC2_bark + (1 | species)
##    Data: s_fine_sm_tr
## 
## REML criterion at convergence: -105.1
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.82146 -0.60354 -0.05828  0.56890  1.78359 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  species  (Intercept) 0.004794 0.06924 
##  Residual             0.001756 0.04190 
## Number of obs: 40, groups:  species, 10
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  0.86280    0.02801  8.00000  30.807 1.34e-09 ***
## PC2_bark    -0.03111    0.01445  8.00000  -2.154   0.0634 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## PC2_bark 0.577
anova(b8)
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)  
## PC2_bark 0.0081441 0.0081441     1     8  4.6387 0.0634 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(b8, oldNames=FALSE)
##                              2.5 %       97.5 %
## sd_(Intercept)|species  0.03856875  0.105999534
## sigma                   0.03317949  0.055235159
## (Intercept)             0.80858974  0.917012810
## PC2_bark               -0.05907760 -0.003150597
b8b <- lm (log(s_fine_sm_tr$k+2) ~ PC2_bark, data=s_fine_sm_tr)

anova(b8b)
## Analysis of Variance Table
## 
## Response: log(s_fine_sm_tr$k + 2)
##           Df   Sum Sq  Mean Sq F value    Pr(>F)    
## PC2_bark   1 0.097088 0.097088  16.762 0.0002134 ***
## Residuals 38 0.220109 0.005792                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(b8b)
## 
## Call:
## lm(formula = log(s_fine_sm_tr$k + 2) ~ PC2_bark, data = s_fine_sm_tr)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.147448 -0.051113 -0.005561  0.050591  0.157434 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.86280    0.01473  58.561  < 2e-16 ***
## PC2_bark    -0.03111    0.00760  -4.094 0.000213 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07611 on 38 degrees of freedom
## Multiple R-squared:  0.3061, Adjusted R-squared:  0.2878 
## F-statistic: 16.76 on 1 and 38 DF,  p-value: 0.0002134
###

s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_big<-s_fine[s_fine$diameter_class == "5.0 cm",]
lb1 <- expression(paste("", R^2 , "= ", 0.1134,"%", "   P = 0.02"))
fine_big_plot2<- ggplot()+ geom_point(data=s_fine_big, aes(y=k, x=PC2,color=growth_form), size=3,alpha = 0.6)+ 
  geom_smooth(method = "lm",size=1, data=s_fine_big, aes(y=k, x=PC2, colour=growth_form,linetype=growth_form),se=FALSE)+ 
  scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 3))+
  scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_y_continuous(limits = c(0,1.5))+
  labs(colour ="Growth forms", title = "(b) Inverterbrates blocked", y= " Decay rate (k) 4.0 cm WD)", 
       x= "Wood traits PC2")+ 
  #annotate("text", x = 0, y = 1.2, size=3, label = lb1,parse=TRUE, colour= "lightcoral") +
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 10),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank(),
        strip.text = element_text(colour = "black", size = 12),
        legend.direction = "vertical", legend.box = "horizontal",
        legend.position = "none",
        legend.text=element_text(size=10,face="bold.italic"),
        legend.background = element_blank(),
        legend.key.width=unit(0.2,"cm"),
        legend.key.height=unit(0.2,"cm"))+
  guides(fill="none")
fine_big_plot2

s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_small<-s_fine[s_fine$diameter_class == "2.5 cm",]
lb1 <- expression(paste("", R^2 , "= ", 35.16,"%", "   P < 0.001"))
lb1b <- expression(paste("", R^2 , "= ", 9.96,"%", "   P = 0.014"))
fine_small_plot2<- ggplot()+ geom_point(data=s_fine_small, aes(y=k, x=PC2, colour=growth_form), size=3,alpha = 0.6)+ 
  geom_smooth(method = "lm",size=1, data=s_fine_small, aes(y=k, x=PC2, colour=growth_form, linetype = growth_form),se=FALSE)+
  scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 3))+
  scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+scale_y_continuous(limits = c(0,1.5))+
  labs(colour ="Growth forms", title = "(a) Inverterbrates blocked", y= " Decay rate (k) 2.5 cm WD", 
       x= "Wood traits PC2")+ guides(shape=FALSE)+
  annotate("text", x = 3, y = 10, size=3, label = as.character(lb1),parse=TRUE, colour= "gray60") +
  annotate("text", x = 3, y = 20, size=3, label = as.character(lb1b),parse=TRUE,colour="black") +
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 10),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank(),
        strip.text = element_text(colour = "black", size = 12),
        legend.direction = "vertical", legend.box = "horizontal",
        legend.position = c(0.3,0.8),
        legend.text=element_text(size=10,face="bold.italic"),
        legend.background = element_blank(),
        legend.key.width=unit(0.2,"cm"),
        legend.key.height=unit(0.2,"cm"))+
  guides(fill="none")

fine_small_plot2

####
s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_big<-s_crs[s_crs$diameter_class == "5.0 cm",]
lb1 <- expression(paste("", R^2 , "= ", 10.94,"%", "   P = 0.03"))
crs_big_plot2<- ggplot()+ geom_point(data=s_crs_big, aes(y=k, x=PC2, colour=growth_form), size=3,alpha = 0.6)+ 
  geom_smooth(method = "lm",size=1, data=s_crs_big, aes(y=k, x=PC2, colour=growth_form,linetype = growth_form),se=FALSE)+ 
  scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 3))+
  scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_y_continuous(limits = c(0,5))+
  #annotate("text", x = 0, y = 1.2, size=3, label = lb1,parse=TRUE, colour= "lightcoral") +
  labs(colour ="Growth forms", title = "(d) Inverterbrates access", y= "Decay rate (k) 4.0 cm WD)", 
       x= "Wood traits PC2")+ guides(shape=FALSE)+
  annotate("text", x = 3, y = 10, size=3, label = as.character(lb1),parse=TRUE, colour= "gray60") +
  annotate("text", x = 3, y = 20, size=3, label = as.character(lb1b),parse=TRUE,colour="black") +
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 10),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank(),
        strip.text = element_text(colour = "black", size = 12),
        legend.direction = "vertical", legend.box = "horizontal",
        legend.position = "none",
        legend.text=element_text(size=10,face="bold.italic"),
        legend.background = element_blank(),
        legend.key.width=unit(0.2,"cm"),
        legend.key.height=unit(0.2,"cm"))+
  guides(fill="none")

crs_big_plot2

s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_small<-s_crs[s_crs$diameter_class == "2.5 cm",]
lb1 <- expression(paste("", R^2 , "= ", 35.16,"%", "   P < 0.001"))
lb1b <- expression(paste("", R^2 , "= ", 9.96,"%", "   P = 0.014"))
crs_small_plot2<- ggplot()+ geom_point(data=s_crs_small, aes(y=k, x=PC2, colour =growth_form), size=3,alpha = 0.6)+ 
  geom_smooth(method = "lm",size=1, data=s_crs_small, aes(y=k, x=PC2, colour=growth_form, linetype = growth_form),se=FALSE)+ 
  scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 3))+
  scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_y_continuous(limits = c(0,5))+
  labs(colour ="Growth forms", title = "(c) Inverterbrates access", y= "Decay rate (k) 2.5 cm WD)", 
       x= "Wood traits PC2")+ guides(shape=FALSE)+
  annotate("text", x = 3, y = 10, size=3, label = as.character(lb1),parse=TRUE, colour= "gray60") +
  annotate("text", x = 3, y = 20, size=3, label = as.character(lb1b),parse=TRUE,colour="black") +
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 10),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank(),
        strip.text = element_text(colour = "black", size = 12),
        legend.direction = "vertical", legend.box = "horizontal",
        legend.position = "none",
        legend.text=element_text(size=10,face="bold.italic"),
        legend.background = element_blank(),
        legend.key.width=unit(0.2,"cm"),
        legend.key.height=unit(0.2,"cm"))+
  guides(fill="none")

plt_pca2<- fine_small_plot2+fine_big_plot2+crs_small_plot2+crs_big_plot2
#ggsave(filename="plt_pca_k2.png", plot=plt_pca2, device="png",
#       path=path, height=6, width=6, units="in", dpi=500)


###########################################################

s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_big<-s_fine[s_fine$diameter_class == "5.0 cm",]
lb1 <- expression(paste("", R^2 , "= ", 23.49,"%", "   P = 0.001"))
fine_big_plot_brk2<- ggplot()+ geom_point(data=s_fine_big, aes(y=k, x=PC2_bark*-1,color=growth_form), size=3,alpha = 0.6)+ 
  geom_smooth(method = "lm",size=1, data=s_fine_big, aes(y=k, x=PC2_bark*-1, colour=growth_form,linetype=growth_form),se=FALSE)+ 
  scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 1))+
  scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  annotate("text", x = 0, y = 1.2, size=3, label = as.character(lb1),parse=TRUE, colour= "#00E5EE") +
  scale_y_continuous(limits = c(0,1.5))+
  labs(colour ="Growth forms", title = "(f) Inverterbrates blocked", y= "Decay rate (k) 4.0 cm WD)", 
       x= "Bark traits PC2")+ guides(shape=FALSE)+
  annotate("text", x = 3, y = 10, size=3, label = as.character(lb1),parse=TRUE, colour= "gray60") +
  annotate("text", x = 3, y = 20, size=3, label = as.character(lb1b),parse=TRUE,colour="black") +
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 10),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank(),
        strip.text = element_text(colour = "black", size = 12),
        legend.direction = "vertical", legend.box = "horizontal",
        legend.position = "none",
        legend.text=element_text(size=10,face="bold.italic"),
        legend.background = element_blank(),
        legend.key.width=unit(0.2,"cm"),
        legend.key.height=unit(0.2,"cm"))+
  guides(fill="none")
fine_big_plot_brk2

s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_small<-s_fine[s_fine$diameter_class == "2.5 cm",]

lb1 <- expression(paste("", R^2 , "= ", 30.61,"%", "   P > 0.001"))
fine_small_plot_brk2<- ggplot()+ geom_point(data=s_fine_small, aes(y=k, x=PC2_bark*-1, colour=growth_form), size=3,alpha = 0.6)+ 
  geom_smooth(method = "lm",size=1, data=s_fine_small, aes(y=k, x=PC2_bark*-1, colour=growth_form, linetype = growth_form),se=FALSE)+
  scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 1))+
  scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+scale_y_continuous(limits = c(0,1.5))+
  annotate("text", x = 0, y = 1.2, size=3, label = as.character(lb1),parse=TRUE, colour= "#00E5EE") +
  labs(colour ="Growth forms", title = "(e) Inverterbrates blocked", y= "Decay rate (k) 2.5 cm WD)", 
       x= "Bark traits PC2")+ guides(shape=FALSE)+
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 10),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank(),
        strip.text = element_text(colour = "black", size = 12),
        legend.direction = "vertical", legend.box = "horizontal",
        legend.position = "none",
        legend.text=element_text(size=10,face="bold.italic"),
        legend.background = element_blank(),
        legend.key.width=unit(0.2,"cm"),
        legend.key.height=unit(0.2,"cm"))+
  guides(fill="none")

fine_small_plot_brk2

####
s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_big<-s_crs[s_crs$diameter_class == "5.0 cm",]
lb1 <- expression(paste("", R^2 , "= ", 23.85,"%", "   P = 0.001"))
crs_big_plot_brk2<- ggplot()+ geom_point(data=s_crs_big, aes(y=k, x=PC2_bark*-1, colour=growth_form), size=3,alpha = 0.6)+ 
  geom_smooth(method = "lm",size=1, data=s_crs_big, aes(y=k, x=PC2_bark*-1, colour=growth_form,linetype = growth_form),se=FALSE)+ 
  scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 1))+
  scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  annotate("text", x = 0, y = 4.3, size=3, label = as.character(lb1),parse=TRUE, colour= "#00E5EE") +
  scale_y_continuous(limits = c(0,5))+
  labs(colour ="Growth forms", title = "(h) Inverterbrates access", y= " Decay rate (k) 4.0 cm WD)", 
       x= "Bark traits PC2")+ guides(shape=FALSE)+
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 10),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank(),
        strip.text = element_text(colour = "black", size = 12),
        legend.direction = "vertical", legend.box = "horizontal",
        legend.position = "none",
        legend.text=element_text(size=10,face="bold.italic"),
        legend.background = element_blank(),
        legend.key.width=unit(0.2,"cm"),
        legend.key.height=unit(0.2,"cm"))+
  guides(fill="none")

crs_big_plot_brk2

s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_small<-s_crs[s_crs$diameter_class == "2.5 cm",]
lb1 <- expression(paste("", R^2 , "= ", 31.07,"%", "   P < 0.001"))
crs_small_plot_brk2<- ggplot()+ geom_point(data=s_crs_small, aes(y=k, x=PC2_bark*-1, colour =growth_form), size=3,alpha = 0.6)+ 
  geom_smooth(method = "lm",size=1, data=s_crs_small, aes(y=k, x=PC2_bark*-1, colour=growth_form, linetype = growth_form),se=FALSE)+ 
  scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 1))+
  scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
  annotate("text", x = -0.5, y = 4.3, size=3, label = as.character(lb1),parse=TRUE, colour= "#00E5EE") +
  scale_y_continuous(limits = c(0,5))+
  labs(colour ="Growth forms", title = " (g) Inverterbrates access", y= "Decay rate (k) 2.5 cm WD)", 
       x= "Bark traits PC2")+ guides(shape=FALSE)+
  theme(axis.line = element_line(colour = "black", size = 0.5),
        axis.ticks.x=element_line(colour = "black"),
        axis.ticks.y=element_line(colour = "black"),
        axis.text = element_text(colour = "black", size = 10),
        axis.title = element_text(colour = "black", size = 12),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
        panel.background = element_blank(),
        strip.text = element_text(colour = "black", size = 12),
        legend.direction = "vertical", legend.box = "horizontal",
        legend.position = "none",
        legend.text=element_text(size=10,face="bold.italic"),
        legend.background = element_blank(),
        legend.key.width=unit(0.2,"cm"),
        legend.key.height=unit(0.2,"cm"))+
  guides(fill="none")
crs_small_plot_brk2

plt_pca_brk2<- fine_small_plot_brk2+fine_big_plot_brk2+crs_small_plot_brk2+crs_big_plot_brk2
#ggsave(filename="plt_pca_k.png", plot=plt_pca, device="png",
#       path=path, height=6, width=6, units="in", dpi=500)

plt_pca_wd_brk2<- fine_small_plot2+fine_big_plot2+crs_small_plot2+crs_big_plot2+
  fine_small_plot_brk2+fine_big_plot_brk2+crs_small_plot_brk2+crs_big_plot_brk2+plot_layout(nrow = 2)

## Plot figure S8 decay rates against wood and bark traits PC2 
ggsave(filename="Figure S8.png", plot=plt_pca_wd_brk2, device="png",
       path=path, height=6, width=12, units="in", dpi=500)

13 Conclusions

We compared wood decomposition rates of 12 lianas species and 12 tree species across different diameter classes in the presence and absence of invertebrates. After 24 months of decomposition, we found that liana wood decomposed faster than tree wood, and that the presence of invertebrates substantially increased wood decay. We also found that liana bark was more resistant to decay and persisted even after the xylem tissues was completely decomposed, which slowed liana decomposition rate. This study highlights the possible negative effects of liana proliferation on forest biogeochemical cycling and its implications for the potential of tropical forests to sequester and store carbon.